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sparse.py

Source:sparse.py

1"""2SparseArray data structure3"""4from __future__ import division5import numbers6import operator7import re8import warnings9import numpy as np10from pandas._libs import index as libindex, lib11import pandas._libs.sparse as splib12from pandas._libs.sparse import BlockIndex, IntIndex13from pandas._libs.tslibs import NaT14import pandas.compat as compat15from pandas.compat.numpy import function as nv16from pandas.errors import PerformanceWarning17from pandas.core.dtypes.base import ExtensionDtype18from pandas.core.dtypes.cast import (19    astype_nansafe, construct_1d_arraylike_from_scalar, find_common_type,20    infer_dtype_from_scalar, maybe_convert_platform)21from pandas.core.dtypes.common import (22    is_array_like, is_bool_dtype, is_datetime64_any_dtype, is_dtype_equal,23    is_integer, is_list_like, is_object_dtype, is_scalar, is_string_dtype,24    pandas_dtype)25from pandas.core.dtypes.dtypes import register_extension_dtype26from pandas.core.dtypes.generic import (27    ABCIndexClass, ABCSeries, ABCSparseSeries)28from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna29from pandas.core.accessor import PandasDelegate, delegate_names30import pandas.core.algorithms as algos31from pandas.core.arrays import ExtensionArray, ExtensionOpsMixin32from pandas.core.base import PandasObject33import pandas.core.common as com34from pandas.core.missing import interpolate_2d35import pandas.io.formats.printing as printing36# ----------------------------------------------------------------------------37# Dtype38@register_extension_dtype39class SparseDtype(ExtensionDtype):40    """41    Dtype for data stored in :class:`SparseArray`.42    This dtype implements the pandas ExtensionDtype interface.43    .. versionadded:: 0.24.044    Parameters45    ----------46    dtype : str, ExtensionDtype, numpy.dtype, type, default numpy.float6447        The dtype of the underlying array storing the non-fill value values.48    fill_value : scalar, optional49        The scalar value not stored in the SparseArray. By default, this50        depends on `dtype`.51        =========== ==========52        dtype       na_value53        =========== ==========54        float       ``np.nan``55        int         ``0``56        bool        ``False``57        datetime64  ``pd.NaT``58        timedelta64 ``pd.NaT``59        =========== ==========60        The default value may be overridden by specifying a `fill_value`.61    """62    # We include `_is_na_fill_value` in the metadata to avoid hash collisions63    # between SparseDtype(float, 0.0) and SparseDtype(float, nan).64    # Without is_na_fill_value in the comparison, those would be equal since65    # hash(nan) is (sometimes?) 0.66    _metadata = ('_dtype', '_fill_value', '_is_na_fill_value')67    def __init__(self, dtype=np.float64, fill_value=None):68        # type: (Union[str, np.dtype, 'ExtensionDtype', type], Any) -> None69        from pandas.core.dtypes.missing import na_value_for_dtype70        from pandas.core.dtypes.common import (71            pandas_dtype, is_string_dtype, is_scalar72        )73        if isinstance(dtype, type(self)):74            if fill_value is None:75                fill_value = dtype.fill_value76            dtype = dtype.subtype77        dtype = pandas_dtype(dtype)78        if is_string_dtype(dtype):79            dtype = np.dtype('object')80        if fill_value is None:81            fill_value = na_value_for_dtype(dtype)82        if not is_scalar(fill_value):83            raise ValueError("fill_value must be a scalar. Got {} "84                             "instead".format(fill_value))85        self._dtype = dtype86        self._fill_value = fill_value87    def __hash__(self):88        # Python3 doesn't inherit __hash__ when a base class overrides89        # __eq__, so we explicitly do it here.90        return super(SparseDtype, self).__hash__()91    def __eq__(self, other):92        # We have to override __eq__ to handle NA values in _metadata.93        # The base class does simple == checks, which fail for NA.94        if isinstance(other, compat.string_types):95            try:96                other = self.construct_from_string(other)97            except TypeError:98                return False99        if isinstance(other, type(self)):100            subtype = self.subtype == other.subtype101            if self._is_na_fill_value:102                # this case is complicated by two things:103                # SparseDtype(float, float(nan)) == SparseDtype(float, np.nan)104                # SparseDtype(float, np.nan)     != SparseDtype(float, pd.NaT)105                # i.e. we want to treat any floating-point NaN as equal, but106                # not a floating-point NaN and a datetime NaT.107                fill_value = (108                    other._is_na_fill_value and109                    isinstance(self.fill_value, type(other.fill_value)) or110                    isinstance(other.fill_value, type(self.fill_value))111                )112            else:113                fill_value = self.fill_value == other.fill_value114            return subtype and fill_value115        return False116    @property117    def fill_value(self):118        """119        The fill value of the array.120        Converting the SparseArray to a dense ndarray will fill the121        array with this value.122        .. warning::123           It's possible to end up with a SparseArray that has ``fill_value``124           values in ``sp_values``. This can occur, for example, when setting125           ``SparseArray.fill_value`` directly.126        """127        return self._fill_value128    @property129    def _is_na_fill_value(self):130        from pandas.core.dtypes.missing import isna131        return isna(self.fill_value)132    @property133    def _is_numeric(self):134        from pandas.core.dtypes.common import is_object_dtype135        return not is_object_dtype(self.subtype)136    @property137    def _is_boolean(self):138        from pandas.core.dtypes.common import is_bool_dtype139        return is_bool_dtype(self.subtype)140    @property141    def kind(self):142        """143        The sparse kind. Either 'integer', or 'block'.144        """145        return self.subtype.kind146    @property147    def type(self):148        return self.subtype.type149    @property150    def subtype(self):151        return self._dtype152    @property153    def name(self):154        return 'Sparse[{}, {}]'.format(self.subtype.name, self.fill_value)155    def __repr__(self):156        return self.name157    @classmethod158    def construct_array_type(cls):159        return SparseArray160    @classmethod161    def construct_from_string(cls, string):162        """163        Construct a SparseDtype from a string form.164        Parameters165        ----------166        string : str167            Can take the following forms.168            string           dtype169            ================ ============================170            'int'            SparseDtype[np.int64, 0]171            'Sparse'         SparseDtype[np.float64, nan]172            'Sparse[int]'    SparseDtype[np.int64, 0]173            'Sparse[int, 0]' SparseDtype[np.int64, 0]174            ================ ============================175            It is not possible to specify non-default fill values176            with a string. An argument like ``'Sparse[int, 1]'``177            will raise a ``TypeError`` because the default fill value178            for integers is 0.179        Returns180        -------181        SparseDtype182        """183        msg = "Could not construct SparseDtype from '{}'".format(string)184        if string.startswith("Sparse"):185            try:186                sub_type, has_fill_value = cls._parse_subtype(string)187                result = SparseDtype(sub_type)188            except Exception:189                raise TypeError(msg)190            else:191                msg = ("Could not construct SparseDtype from '{}'.\n\nIt "192                       "looks like the fill_value in the string is not "193                       "the default for the dtype. Non-default fill_values "194                       "are not supported. Use the 'SparseDtype()' "195                       "constructor instead.")196                if has_fill_value and str(result) != string:197                    raise TypeError(msg.format(string))198                return result199        else:200            raise TypeError(msg)201    @staticmethod202    def _parse_subtype(dtype):203        """204        Parse a string to get the subtype205        Parameters206        ----------207        dtype : str208            A string like209            * Sparse[subtype]210            * Sparse[subtype, fill_value]211        Returns212        -------213        subtype : str214        Raises215        ------216        ValueError217            When the subtype cannot be extracted.218        """219        xpr = re.compile(220            r"Sparse\[(?P<subtype>[^,]*)(, )?(?P<fill_value>.*?)?\]$"221        )222        m = xpr.match(dtype)223        has_fill_value = False224        if m:225            subtype = m.groupdict()['subtype']226            has_fill_value = m.groupdict()['fill_value'] or has_fill_value227        elif dtype == "Sparse":228            subtype = 'float64'229        else:230            raise ValueError("Cannot parse {}".format(dtype))231        return subtype, has_fill_value232    @classmethod233    def is_dtype(cls, dtype):234        dtype = getattr(dtype, 'dtype', dtype)235        if (isinstance(dtype, compat.string_types) and236                dtype.startswith("Sparse")):237            sub_type, _ = cls._parse_subtype(dtype)238            dtype = np.dtype(sub_type)239        elif isinstance(dtype, cls):240            return True241        return isinstance(dtype, np.dtype) or dtype == 'Sparse'242    def update_dtype(self, dtype):243        """244        Convert the SparseDtype to a new dtype.245        This takes care of converting the ``fill_value``.246        Parameters247        ----------248        dtype : Union[str, numpy.dtype, SparseDtype]249            The new dtype to use.250            * For a SparseDtype, it is simply returned251            * For a NumPy dtype (or str), the current fill value252              is converted to the new dtype, and a SparseDtype253              with `dtype` and the new fill value is returned.254        Returns255        -------256        SparseDtype257            A new SparseDtype with the corret `dtype` and fill value258            for that `dtype`.259        Raises260        ------261        ValueError262            When the current fill value cannot be converted to the263            new `dtype` (e.g. trying to convert ``np.nan`` to an264            integer dtype).265        Examples266        --------267        >>> SparseDtype(int, 0).update_dtype(float)268        Sparse[float64, 0.0]269        >>> SparseDtype(int, 1).update_dtype(SparseDtype(float, np.nan))270        Sparse[float64, nan]271        """272        cls = type(self)273        dtype = pandas_dtype(dtype)274        if not isinstance(dtype, cls):275            fill_value = astype_nansafe(np.array(self.fill_value),276                                        dtype).item()277            dtype = cls(dtype, fill_value=fill_value)278        return dtype279    @property280    def _subtype_with_str(self):281        """282        Whether the SparseDtype's subtype should be considered ``str``.283        Typically, pandas will store string data in an object-dtype array.284        When converting values to a dtype, e.g. in ``.astype``, we need to285        be more specific, we need the actual underlying type.286        Returns287        -------288        >>> SparseDtype(int, 1)._subtype_with_str289        dtype('int64')290        >>> SparseDtype(object, 1)._subtype_with_str291        dtype('O')292        >>> dtype = SparseDtype(str, '')293        >>> dtype.subtype294        dtype('O')295        >>> dtype._subtype_with_str296        str297        """298        if isinstance(self.fill_value, compat.string_types):299            return type(self.fill_value)300        return self.subtype301# ----------------------------------------------------------------------------302# Array303_sparray_doc_kwargs = dict(klass='SparseArray')304def _get_fill(arr):305    # type: (SparseArray) -> ndarray306    """307    Create a 0-dim ndarray containing the fill value308    Parameters309    ----------310    arr : SparseArray311    Returns312    -------313    fill_value : ndarray314        0-dim ndarray with just the fill value.315    Notes316    -----317    coerce fill_value to arr dtype if possible318    int64 SparseArray can have NaN as fill_value if there is no missing319    """320    try:321        return np.asarray(arr.fill_value, dtype=arr.dtype.subtype)322    except ValueError:323        return np.asarray(arr.fill_value)324def _sparse_array_op(left, right, op, name):325    """326    Perform a binary operation between two arrays.327    Parameters328    ----------329    left : Union[SparseArray, ndarray]330    right : Union[SparseArray, ndarray]331    op : Callable332        The binary operation to perform333    name str334        Name of the callable.335    Returns336    -------337    SparseArray338    """339    # type: (SparseArray, SparseArray, Callable, str) -> Any340    if name.startswith('__'):341        # For lookups in _libs.sparse we need non-dunder op name342        name = name[2:-2]343    # dtype used to find corresponding sparse method344    ltype = left.dtype.subtype345    rtype = right.dtype.subtype346    if not is_dtype_equal(ltype, rtype):347        subtype = find_common_type([ltype, rtype])348        ltype = SparseDtype(subtype, left.fill_value)349        rtype = SparseDtype(subtype, right.fill_value)350        # TODO(GH-23092): pass copy=False. Need to fix astype_nansafe351        left = left.astype(ltype)352        right = right.astype(rtype)353        dtype = ltype.subtype354    else:355        dtype = ltype356    # dtype the result must have357    result_dtype = None358    if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:359        with np.errstate(all='ignore'):360            result = op(left.get_values(), right.get_values())361            fill = op(_get_fill(left), _get_fill(right))362        if left.sp_index.ngaps == 0:363            index = left.sp_index364        else:365            index = right.sp_index366    elif left.sp_index.equals(right.sp_index):367        with np.errstate(all='ignore'):368            result = op(left.sp_values, right.sp_values)369            fill = op(_get_fill(left), _get_fill(right))370        index = left.sp_index371    else:372        if name[0] == 'r':373            left, right = right, left374            name = name[1:]375        if name in ('and', 'or') and dtype == 'bool':376            opname = 'sparse_{name}_uint8'.format(name=name)377            # to make template simple, cast here378            left_sp_values = left.sp_values.view(np.uint8)379            right_sp_values = right.sp_values.view(np.uint8)380            result_dtype = np.bool381        else:382            opname = 'sparse_{name}_{dtype}'.format(name=name, dtype=dtype)383            left_sp_values = left.sp_values384            right_sp_values = right.sp_values385        sparse_op = getattr(splib, opname)386        with np.errstate(all='ignore'):387            result, index, fill = sparse_op(388                left_sp_values, left.sp_index, left.fill_value,389                right_sp_values, right.sp_index, right.fill_value)390    if result_dtype is None:391        result_dtype = result.dtype392    return _wrap_result(name, result, index, fill, dtype=result_dtype)393def _wrap_result(name, data, sparse_index, fill_value, dtype=None):394    """395    wrap op result to have correct dtype396    """397    if name.startswith('__'):398        # e.g. __eq__ --> eq399        name = name[2:-2]400    if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):401        dtype = np.bool402    fill_value = lib.item_from_zerodim(fill_value)403    if is_bool_dtype(dtype):404        # fill_value may be np.bool_405        fill_value = bool(fill_value)406    return SparseArray(data,407                       sparse_index=sparse_index,408                       fill_value=fill_value,409                       dtype=dtype)410class SparseArray(PandasObject, ExtensionArray, ExtensionOpsMixin):411    """412    An ExtensionArray for storing sparse data.413    .. versionchanged:: 0.24.0414       Implements the ExtensionArray interface.415    Parameters416    ----------417    data : array-like418        A dense array of values to store in the SparseArray. This may contain419        `fill_value`.420    sparse_index : SparseIndex, optional421    index : Index422    fill_value : scalar, optional423        Elements in `data` that are `fill_value` are not stored in the424        SparseArray. For memory savings, this should be the most common value425        in `data`. By default, `fill_value` depends on the dtype of `data`:426        =========== ==========427        data.dtype  na_value428        =========== ==========429        float       ``np.nan``430        int         ``0``431        bool        False432        datetime64  ``pd.NaT``433        timedelta64 ``pd.NaT``434        =========== ==========435        The fill value is potentiall specified in three ways. In order of436        precedence, these are437        1. The `fill_value` argument438        2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is439           a ``SparseDtype``440        3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`441           is not a ``SparseDtype`` and `data` is a ``SparseArray``.442    kind : {'integer', 'block'}, default 'integer'443        The type of storage for sparse locations.444        * 'block': Stores a `block` and `block_length` for each445          contiguous *span* of sparse values. This is best when446          sparse data tends to be clumped together, with large447          regsions of ``fill-value`` values between sparse values.448        * 'integer': uses an integer to store the location of449          each sparse value.450    dtype : np.dtype or SparseDtype, optional451        The dtype to use for the SparseArray. For numpy dtypes, this452        determines the dtype of ``self.sp_values``. For SparseDtype,453        this determines ``self.sp_values`` and ``self.fill_value``.454    copy : bool, default False455        Whether to explicitly copy the incoming `data` array.456    """457    __array_priority__ = 15458    _pandas_ftype = 'sparse'459    _subtyp = 'sparse_array'  # register ABCSparseArray460    def __init__(self, data, sparse_index=None, index=None, fill_value=None,461                 kind='integer', dtype=None, copy=False):462        from pandas.core.internals import SingleBlockManager463        if isinstance(data, SingleBlockManager):464            data = data.internal_values()465        if fill_value is None and isinstance(dtype, SparseDtype):466            fill_value = dtype.fill_value467        if isinstance(data, (type(self), ABCSparseSeries)):468            # disable normal inference on dtype, sparse_index, & fill_value469            if sparse_index is None:470                sparse_index = data.sp_index471            if fill_value is None:472                fill_value = data.fill_value473            if dtype is None:474                dtype = data.dtype475            # TODO: make kind=None, and use data.kind?476            data = data.sp_values477        # Handle use-provided dtype478        if isinstance(dtype, compat.string_types):479            # Two options: dtype='int', regular numpy dtype480            # or dtype='Sparse[int]', a sparse dtype481            try:482                dtype = SparseDtype.construct_from_string(dtype)483            except TypeError:484                dtype = pandas_dtype(dtype)485        if isinstance(dtype, SparseDtype):486            if fill_value is None:487                fill_value = dtype.fill_value488            dtype = dtype.subtype489        if index is not None and not is_scalar(data):490            raise Exception("must only pass scalars with an index ")491        if is_scalar(data):492            if index is not None:493                if data is None:494                    data = np.nan495            if index is not None:496                npoints = len(index)497            elif sparse_index is None:498                npoints = 1499            else:500                npoints = sparse_index.length501            dtype = infer_dtype_from_scalar(data)[0]502            data = construct_1d_arraylike_from_scalar(503                data, npoints, dtype504            )505        if dtype is not None:506            dtype = pandas_dtype(dtype)507        # TODO: disentangle the fill_value dtype inference from508        # dtype inference509        if data is None:510            # XXX: What should the empty dtype be? Object or float?511            data = np.array([], dtype=dtype)512        if not is_array_like(data):513            try:514                # probably shared code in sanitize_series515                from pandas.core.internals.construction import sanitize_array516                data = sanitize_array(data, index=None)517            except ValueError:518                # NumPy may raise a ValueError on data like [1, []]519                # we retry with object dtype here.520                if dtype is None:521                    dtype = object522                    data = np.atleast_1d(np.asarray(data, dtype=dtype))523                else:524                    raise525        if copy:526            # TODO: avoid double copy when dtype forces cast.527            data = data.copy()528        if fill_value is None:529            fill_value_dtype = data.dtype if dtype is None else dtype530            if fill_value_dtype is None:531                fill_value = np.nan532            else:533                fill_value = na_value_for_dtype(fill_value_dtype)534        if isinstance(data, type(self)) and sparse_index is None:535            sparse_index = data._sparse_index536            sparse_values = np.asarray(data.sp_values, dtype=dtype)537        elif sparse_index is None:538            sparse_values, sparse_index, fill_value = make_sparse(539                data, kind=kind, fill_value=fill_value, dtype=dtype540            )541        else:542            sparse_values = np.asarray(data, dtype=dtype)543            if len(sparse_values) != sparse_index.npoints:544                raise AssertionError("Non array-like type {type} must "545                                     "have the same length as the index"546                                     .format(type=type(sparse_values)))547        self._sparse_index = sparse_index548        self._sparse_values = sparse_values549        self._dtype = SparseDtype(sparse_values.dtype, fill_value)550    @classmethod551    def _simple_new(cls, sparse_array, sparse_index, dtype):552        # type: (np.ndarray, SparseIndex, SparseDtype) -> 'SparseArray'553        new = cls([])554        new._sparse_index = sparse_index555        new._sparse_values = sparse_array556        new._dtype = dtype557        return new558    def __array__(self, dtype=None, copy=True):559        fill_value = self.fill_value560        if self.sp_index.ngaps == 0:561            # Compat for na dtype and int values.562            return self.sp_values563        if dtype is None:564            # Can NumPy represent this type?565            # If not, `np.result_type` will raise. We catch that566            # and return object.567            if is_datetime64_any_dtype(self.sp_values.dtype):568                # However, we *do* special-case the common case of569                # a datetime64 with pandas NaT.570                if fill_value is NaT:571                    # Can't put pd.NaT in a datetime64[ns]572                    fill_value = np.datetime64('NaT')573            try:574                dtype = np.result_type(self.sp_values.dtype, type(fill_value))575            except TypeError:576                dtype = object577        out = np.full(self.shape, fill_value, dtype=dtype)578        out[self.sp_index.to_int_index().indices] = self.sp_values579        return out580    def __setitem__(self, key, value):581        # I suppose we could allow setting of non-fill_value elements.582        # TODO(SparseArray.__setitem__): remove special cases in583        # ExtensionBlock.where584        msg = "SparseArray does not support item assignment via setitem"585        raise TypeError(msg)586    @classmethod587    def _from_sequence(cls, scalars, dtype=None, copy=False):588        return cls(scalars, dtype=dtype)589    @classmethod590    def _from_factorized(cls, values, original):591        return cls(values, dtype=original.dtype)592    # ------------------------------------------------------------------------593    # Data594    # ------------------------------------------------------------------------595    @property596    def sp_index(self):597        """598        The SparseIndex containing the location of non- ``fill_value`` points.599        """600        return self._sparse_index601    @property602    def sp_values(self):603        """604        An ndarray containing the non- ``fill_value`` values.605        Examples606        --------607        >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)608        >>> s.sp_values609        array([1, 2])610        """611        return self._sparse_values612    @property613    def dtype(self):614        return self._dtype615    @property616    def fill_value(self):617        """618        Elements in `data` that are `fill_value` are not stored.619        For memory savings, this should be the most common value in the array.620        """621        return self.dtype.fill_value622    @fill_value.setter623    def fill_value(self, value):624        self._dtype = SparseDtype(self.dtype.subtype, value)625    @property626    def kind(self):627        """628        The kind of sparse index for this array. One of {'integer', 'block'}.629        """630        if isinstance(self.sp_index, IntIndex):631            return 'integer'632        else:633            return 'block'634    @property635    def _valid_sp_values(self):636        sp_vals = self.sp_values637        mask = notna(sp_vals)638        return sp_vals[mask]639    def __len__(self):640        return self.sp_index.length641    @property642    def _null_fill_value(self):643        return self._dtype._is_na_fill_value644    def _fill_value_matches(self, fill_value):645        if self._null_fill_value:646            return isna(fill_value)647        else:648            return self.fill_value == fill_value649    @property650    def nbytes(self):651        return self.sp_values.nbytes + self.sp_index.nbytes652    @property653    def density(self):654        """655        The percent of non- ``fill_value`` points, as decimal.656        Examples657        --------658        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)659        >>> s.density660        0.6661        """662        r = float(self.sp_index.npoints) / float(self.sp_index.length)663        return r664    @property665    def npoints(self):666        """667        The number of non- ``fill_value`` points.668        Examples669        --------670        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)671        >>> s.npoints672        3673        """674        return self.sp_index.npoints675    @property676    def values(self):677        """678        Dense values679        """680        return self.to_dense()681    def isna(self):682        from pandas import isna683        # If null fill value, we want SparseDtype[bool, true]684        # to preserve the same memory usage.685        dtype = SparseDtype(bool, self._null_fill_value)686        return type(self)._simple_new(isna(self.sp_values),687                                      self.sp_index, dtype)688    def fillna(self, value=None, method=None, limit=None):689        """690        Fill missing values with `value`.691        Parameters692        ----------693        value : scalar, optional694        method : str, optional695            .. warning::696               Using 'method' will result in high memory use,697               as all `fill_value` methods will be converted to698               an in-memory ndarray699        limit : int, optional700        Returns701        -------702        SparseArray703        Notes704        -----705        When `value` is specified, the result's ``fill_value`` depends on706        ``self.fill_value``. The goal is to maintain low-memory use.707        If ``self.fill_value`` is NA, the result dtype will be708        ``SparseDtype(self.dtype, fill_value=value)``. This will preserve709        amount of memory used before and after filling.710        When ``self.fill_value`` is not NA, the result dtype will be711        ``self.dtype``. Again, this preserves the amount of memory used.712        """713        if ((method is None and value is None) or714                (method is not None and value is not None)):715            raise ValueError("Must specify one of 'method' or 'value'.")716        elif method is not None:717            msg = "fillna with 'method' requires high memory usage."718            warnings.warn(msg, PerformanceWarning)719            filled = interpolate_2d(np.asarray(self), method=method,720                                    limit=limit)721            return type(self)(filled, fill_value=self.fill_value)722        else:723            new_values = np.where(isna(self.sp_values), value, self.sp_values)724            if self._null_fill_value:725                # This is essentially just updating the dtype.726                new_dtype = SparseDtype(self.dtype.subtype, fill_value=value)727            else:728                new_dtype = self.dtype729        return self._simple_new(new_values, self._sparse_index, new_dtype)730    def shift(self, periods=1, fill_value=None):731        if not len(self) or periods == 0:732            return self.copy()733        if isna(fill_value):734            fill_value = self.dtype.na_value735        subtype = np.result_type(fill_value, self.dtype.subtype)736        if subtype != self.dtype.subtype:737            # just coerce up front738            arr = self.astype(SparseDtype(subtype, self.fill_value))739        else:740            arr = self741        empty = self._from_sequence(742            [fill_value] * min(abs(periods), len(self)),743            dtype=arr.dtype744        )745        if periods > 0:746            a = empty747            b = arr[:-periods]748        else:749            a = arr[abs(periods):]750            b = empty751        return arr._concat_same_type([a, b])752    def _first_fill_value_loc(self):753        """754        Get the location of the first missing value.755        Returns756        -------757        int758        """759        if len(self) == 0 or self.sp_index.npoints == len(self):760            return -1761        indices = self.sp_index.to_int_index().indices762        if not len(indices) or indices[0] > 0:763            return 0764        diff = indices[1:] - indices[:-1]765        return np.searchsorted(diff, 2) + 1766    def unique(self):767        uniques = list(algos.unique(self.sp_values))768        fill_loc = self._first_fill_value_loc()769        if fill_loc >= 0:770            uniques.insert(fill_loc, self.fill_value)771        return type(self)._from_sequence(uniques, dtype=self.dtype)772    def _values_for_factorize(self):773        # Still override this for hash_pandas_object774        return np.asarray(self), self.fill_value775    def factorize(self, na_sentinel=-1):776        # Currently, ExtensionArray.factorize -> Tuple[ndarray, EA]777        # The sparsity on this is backwards from what Sparse would want. Want778        # ExtensionArray.factorize -> Tuple[EA, EA]779        # Given that we have to return a dense array of labels, why bother780        # implementing an efficient factorize?781        labels, uniques = algos.factorize(np.asarray(self),782                                          na_sentinel=na_sentinel)783        uniques = SparseArray(uniques, dtype=self.dtype)784        return labels, uniques785    def value_counts(self, dropna=True):786        """787        Returns a Series containing counts of unique values.788        Parameters789        ----------790        dropna : boolean, default True791            Don't include counts of NaN, even if NaN is in sp_values.792        Returns793        -------794        counts : Series795        """796        from pandas import Index, Series797        keys, counts = algos._value_counts_arraylike(self.sp_values,798                                                     dropna=dropna)799        fcounts = self.sp_index.ngaps800        if fcounts > 0:801            if self._null_fill_value and dropna:802                pass803            else:804                if self._null_fill_value:805                    mask = isna(keys)806                else:807                    mask = keys == self.fill_value808                if mask.any():809                    counts[mask] += fcounts810                else:811                    keys = np.insert(keys, 0, self.fill_value)812                    counts = np.insert(counts, 0, fcounts)813        if not isinstance(keys, ABCIndexClass):814            keys = Index(keys)815        result = Series(counts, index=keys)816        return result817    # --------818    # Indexing819    # --------820    def __getitem__(self, key):821        if isinstance(key, tuple):822            if len(key) > 1:823                raise IndexError("too many indices for array.")824            key = key[0]825        if is_integer(key):826            return self._get_val_at(key)827        elif isinstance(key, tuple):828            data_slice = self.values[key]829        elif isinstance(key, slice):830            # special case to preserve dtypes831            if key == slice(None):832                return self.copy()833            # TODO: this logic is surely elsewhere834            # TODO: this could be more efficient835            indices = np.arange(len(self), dtype=np.int32)[key]836            return self.take(indices)837        else:838            # TODO: I think we can avoid densifying when masking a839            # boolean SparseArray with another. Need to look at the840            # key's fill_value for True / False, and then do an intersection841            # on the indicies of the sp_values.842            if isinstance(key, SparseArray):843                if is_bool_dtype(key):844                    key = key.to_dense()845                else:846                    key = np.asarray(key)847            if com.is_bool_indexer(key) and len(self) == len(key):848                return self.take(np.arange(len(key), dtype=np.int32)[key])849            elif hasattr(key, '__len__'):850                return self.take(key)851            else:852                raise ValueError("Cannot slice with '{}'".format(key))853        return type(self)(data_slice, kind=self.kind)854    def _get_val_at(self, loc):855        n = len(self)856        if loc < 0:857            loc += n858        if loc >= n or loc < 0:859            raise IndexError('Out of bounds access')860        sp_loc = self.sp_index.lookup(loc)861        if sp_loc == -1:862            return self.fill_value863        else:864            return libindex.get_value_at(self.sp_values, sp_loc)865    def take(self, indices, allow_fill=False, fill_value=None):866        if is_scalar(indices):867            raise ValueError("'indices' must be an array, not a "868                             "scalar '{}'.".format(indices))869        indices = np.asarray(indices, dtype=np.int32)870        if indices.size == 0:871            result = []872            kwargs = {'dtype': self.dtype}873        elif allow_fill:874            result = self._take_with_fill(indices, fill_value=fill_value)875            kwargs = {}876        else:877            result = self._take_without_fill(indices)878            kwargs = {'dtype': self.dtype}879        return type(self)(result, fill_value=self.fill_value, kind=self.kind,880                          **kwargs)881    def _take_with_fill(self, indices, fill_value=None):882        if fill_value is None:883            fill_value = self.dtype.na_value884        if indices.min() < -1:885            raise ValueError("Invalid value in 'indices'. Must be between -1 "886                             "and the length of the array.")887        if indices.max() >= len(self):888            raise IndexError("out of bounds value in 'indices'.")889        if len(self) == 0:890            # Empty... Allow taking only if all empty891            if (indices == -1).all():892                dtype = np.result_type(self.sp_values, type(fill_value))893                taken = np.empty_like(indices, dtype=dtype)894                taken.fill(fill_value)895                return taken896            else:897                raise IndexError('cannot do a non-empty take from an empty '898                                 'axes.')899        sp_indexer = self.sp_index.lookup_array(indices)900        if self.sp_index.npoints == 0:901            # Avoid taking from the empty self.sp_values902            taken = np.full(sp_indexer.shape, fill_value=fill_value,903                            dtype=np.result_type(type(fill_value)))904        else:905            taken = self.sp_values.take(sp_indexer)906            # sp_indexer may be -1 for two reasons907            # 1.) we took for an index of -1 (new)908            # 2.) we took a value that was self.fill_value (old)909            new_fill_indices = indices == -1910            old_fill_indices = (sp_indexer == -1) & ~new_fill_indices911            # Fill in two steps.912            # Old fill values913            # New fill values914            # potentially coercing to a new dtype at each stage.915            m0 = sp_indexer[old_fill_indices] < 0916            m1 = sp_indexer[new_fill_indices] < 0917            result_type = taken.dtype918            if m0.any():919                result_type = np.result_type(result_type,920                                             type(self.fill_value))921                taken = taken.astype(result_type)922                taken[old_fill_indices] = self.fill_value923            if m1.any():924                result_type = np.result_type(result_type, type(fill_value))925                taken = taken.astype(result_type)926                taken[new_fill_indices] = fill_value927        return taken928    def _take_without_fill(self, indices):929        to_shift = indices < 0930        indices = indices.copy()931        n = len(self)932        if (indices.max() >= n) or (indices.min() < -n):933            if n == 0:934                raise IndexError("cannot do a non-empty take from an "935                                 "empty axes.")936            else:937                raise IndexError("out of bounds value in 'indices'.")938        if to_shift.any():939            indices[to_shift] += n940        if self.sp_index.npoints == 0:941            # edge case in take...942            # I think just return943            out = np.full(indices.shape, self.fill_value,944                          dtype=np.result_type(type(self.fill_value)))945            arr, sp_index, fill_value = make_sparse(out,946                                                    fill_value=self.fill_value)947            return type(self)(arr, sparse_index=sp_index,948                              fill_value=fill_value)949        sp_indexer = self.sp_index.lookup_array(indices)950        taken = self.sp_values.take(sp_indexer)951        fillable = (sp_indexer < 0)952        if fillable.any():953            # TODO: may need to coerce array to fill value954            result_type = np.result_type(taken, type(self.fill_value))955            taken = taken.astype(result_type)956            taken[fillable] = self.fill_value957        return taken958    def searchsorted(self, v, side="left", sorter=None):959        msg = "searchsorted requires high memory usage."960        warnings.warn(msg, PerformanceWarning, stacklevel=2)961        if not is_scalar(v):962            v = np.asarray(v)963        v = np.asarray(v)964        return np.asarray(self, dtype=self.dtype.subtype).searchsorted(965            v, side, sorter966        )967    def copy(self, deep=False):968        if deep:969            values = self.sp_values.copy()970        else:971            values = self.sp_values972        return self._simple_new(values, self.sp_index, self.dtype)973    @classmethod974    def _concat_same_type(cls, to_concat):975        fill_values = [x.fill_value for x in to_concat]976        fill_value = fill_values[0]977        # np.nan isn't a singleton, so we may end up with multiple978        # NaNs here, so we ignore tha all NA case too.979        if not (len(set(fill_values)) == 1 or isna(fill_values).all()):980            warnings.warn("Concatenating sparse arrays with multiple fill "981                          "values: '{}'. Picking the first and "982                          "converting the rest.".format(fill_values),983                          PerformanceWarning,984                          stacklevel=6)985            keep = to_concat[0]986            to_concat2 = [keep]987            for arr in to_concat[1:]:988                to_concat2.append(cls(np.asarray(arr), fill_value=fill_value))989            to_concat = to_concat2990        values = []991        length = 0992        if to_concat:993            sp_kind = to_concat[0].kind994        else:995            sp_kind = 'integer'996        if sp_kind == 'integer':997            indices = []998            for arr in to_concat:999                idx = arr.sp_index.to_int_index().indices.copy()1000                idx += length  # TODO: wraparound1001                length += arr.sp_index.length1002                values.append(arr.sp_values)1003                indices.append(idx)1004            data = np.concatenate(values)1005            indices = np.concatenate(indices)1006            sp_index = IntIndex(length, indices)1007        else:1008            # when concatentating block indices, we don't claim that you'll1009            # get an identical index as concating the values and then1010            # creating a new index. We don't want to spend the time trying1011            # to merge blocks across arrays in `to_concat`, so the resulting1012            # BlockIndex may have more blocs.1013            blengths = []1014            blocs = []1015            for arr in to_concat:1016                idx = arr.sp_index.to_block_index()1017                values.append(arr.sp_values)1018                blocs.append(idx.blocs.copy() + length)1019                blengths.append(idx.blengths)1020                length += arr.sp_index.length1021            data = np.concatenate(values)1022            blocs = np.concatenate(blocs)1023            blengths = np.concatenate(blengths)1024            sp_index = BlockIndex(length, blocs, blengths)1025        return cls(data, sparse_index=sp_index, fill_value=fill_value)1026    def astype(self, dtype=None, copy=True):1027        """1028        Change the dtype of a SparseArray.1029        The output will always be a SparseArray. To convert to a dense1030        ndarray with a certain dtype, use :meth:`numpy.asarray`.1031        Parameters1032        ----------1033        dtype : np.dtype or ExtensionDtype1034            For SparseDtype, this changes the dtype of1035            ``self.sp_values`` and the ``self.fill_value``.1036            For other dtypes, this only changes the dtype of1037            ``self.sp_values``.1038        copy : bool, default True1039            Whether to ensure a copy is made, even if not necessary.1040        Returns1041        -------1042        SparseArray1043        Examples1044        --------1045        >>> arr = SparseArray([0, 0, 1, 2])1046        >>> arr1047        [0, 0, 1, 2]1048        Fill: 01049        IntIndex1050        Indices: array([2, 3], dtype=int32)1051        >>> arr.astype(np.dtype('int32'))1052        [0, 0, 1, 2]1053        Fill: 01054        IntIndex1055        Indices: array([2, 3], dtype=int32)1056        Using a NumPy dtype with a different kind (e.g. float) will coerce1057        just ``self.sp_values``.1058        >>> arr.astype(np.dtype('float64'))1059        ... # doctest: +NORMALIZE_WHITESPACE1060        [0, 0, 1.0, 2.0]1061        Fill: 01062        IntIndex1063        Indices: array([2, 3], dtype=int32)1064        Use a SparseDtype if you wish to be change the fill value as well.1065        >>> arr.astype(SparseDtype("float64", fill_value=np.nan))1066        ... # doctest: +NORMALIZE_WHITESPACE1067        [nan, nan, 1.0, 2.0]1068        Fill: nan1069        IntIndex1070        Indices: array([2, 3], dtype=int32)1071        """1072        dtype = self.dtype.update_dtype(dtype)1073        subtype = dtype._subtype_with_str1074        sp_values = astype_nansafe(self.sp_values,1075                                   subtype,1076                                   copy=copy)1077        if sp_values is self.sp_values and copy:1078            sp_values = sp_values.copy()1079        return self._simple_new(sp_values,1080                                self.sp_index,1081                                dtype)1082    def map(self, mapper):1083        """1084        Map categories using input correspondence (dict, Series, or function).1085        Parameters1086        ----------1087        mapper : dict, Series, callable1088            The correspondence from old values to new.1089        Returns1090        -------1091        SparseArray1092            The output array will have the same density as the input.1093            The output fill value will be the result of applying the1094            mapping to ``self.fill_value``1095        Examples1096        --------1097        >>> arr = pd.SparseArray([0, 1, 2])1098        >>> arr.apply(lambda x: x + 10)1099        [10, 11, 12]1100        Fill: 101101        IntIndex1102        Indices: array([1, 2], dtype=int32)1103        >>> arr.apply({0: 10, 1: 11, 2: 12})1104        [10, 11, 12]1105        Fill: 101106        IntIndex1107        Indices: array([1, 2], dtype=int32)1108        >>> arr.apply(pd.Series([10, 11, 12], index=[0, 1, 2]))1109        [10, 11, 12]1110        Fill: 101111        IntIndex1112        Indices: array([1, 2], dtype=int32)1113        """1114        # this is used in apply.1115        # We get hit since we're an "is_extension_type" but regular extension1116        # types are not hit. This may be worth adding to the interface.1117        if isinstance(mapper, ABCSeries):1118            mapper = mapper.to_dict()1119        if isinstance(mapper, compat.Mapping):1120            fill_value = mapper.get(self.fill_value, self.fill_value)1121            sp_values = [mapper.get(x, None) for x in self.sp_values]1122        else:1123            fill_value = mapper(self.fill_value)1124            sp_values = [mapper(x) for x in self.sp_values]1125        return type(self)(sp_values, sparse_index=self.sp_index,1126                          fill_value=fill_value)1127    def to_dense(self):1128        """1129        Convert SparseArray to a NumPy array.1130        Returns1131        -------1132        arr : NumPy array1133        """1134        return np.asarray(self, dtype=self.sp_values.dtype)1135    # TODO: Look into deprecating this in favor of `to_dense`.1136    get_values = to_dense1137    # ------------------------------------------------------------------------1138    # IO1139    # ------------------------------------------------------------------------1140    def __setstate__(self, state):1141        """Necessary for making this object picklable"""1142        if isinstance(state, tuple):1143            # Compat for pandas < 0.24.01144            nd_state, (fill_value, sp_index) = state1145            sparse_values = np.array([])1146            sparse_values.__setstate__(nd_state)1147            self._sparse_values = sparse_values1148            self._sparse_index = sp_index1149            self._dtype = SparseDtype(sparse_values.dtype, fill_value)1150        else:1151            self.__dict__.update(state)1152    def nonzero(self):1153        if self.fill_value == 0:1154            return self.sp_index.to_int_index().indices,1155        else:1156            return self.sp_index.to_int_index().indices[self.sp_values != 0],1157    # ------------------------------------------------------------------------1158    # Reductions1159    # ------------------------------------------------------------------------1160    def _reduce(self, name, skipna=True, **kwargs):1161        method = getattr(self, name, None)1162        if method is None:1163            raise TypeError("cannot perform {name} with type {dtype}".format(1164                name=name, dtype=self.dtype))1165        if skipna:1166            arr = self1167        else:1168            arr = self.dropna()1169        # we don't support these kwargs.1170        # They should only be present when called via pandas, so do it here.1171        # instead of in `any` / `all` (which will raise if they're present,1172        # thanks to nv.validate1173        kwargs.pop('filter_type', None)1174        kwargs.pop('numeric_only', None)1175        kwargs.pop('op', None)1176        return getattr(arr, name)(**kwargs)1177    def all(self, axis=None, *args, **kwargs):1178        """1179        Tests whether all elements evaluate True1180        Returns1181        -------1182        all : bool1183        See Also1184        --------1185        numpy.all1186        """1187        nv.validate_all(args, kwargs)1188        values = self.sp_values1189        if len(values) != len(self) and not np.all(self.fill_value):1190            return False1191        return values.all()1192    def any(self, axis=0, *args, **kwargs):1193        """1194        Tests whether at least one of elements evaluate True1195        Returns1196        -------1197        any : bool1198        See Also1199        --------1200        numpy.any1201        """1202        nv.validate_any(args, kwargs)1203        values = self.sp_values1204        if len(values) != len(self) and np.any(self.fill_value):1205            return True1206        return values.any().item()1207    def sum(self, axis=0, *args, **kwargs):1208        """1209        Sum of non-NA/null values1210        Returns1211        -------1212        sum : float1213        """1214        nv.validate_sum(args, kwargs)1215        valid_vals = self._valid_sp_values1216        sp_sum = valid_vals.sum()1217        if self._null_fill_value:1218            return sp_sum1219        else:1220            nsparse = self.sp_index.ngaps1221            return sp_sum + self.fill_value * nsparse1222    def cumsum(self, axis=0, *args, **kwargs):1223        """1224        Cumulative sum of non-NA/null values.1225        When performing the cumulative summation, any non-NA/null values will1226        be skipped. The resulting SparseArray will preserve the locations of1227        NaN values, but the fill value will be `np.nan` regardless.1228        Parameters1229        ----------1230        axis : int or None1231            Axis over which to perform the cumulative summation. If None,1232            perform cumulative summation over flattened array.1233        Returns1234        -------1235        cumsum : SparseArray1236        """1237        nv.validate_cumsum(args, kwargs)1238        if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.1239            raise ValueError("axis(={axis}) out of bounds".format(axis=axis))1240        if not self._null_fill_value:1241            return SparseArray(self.to_dense()).cumsum()1242        return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index,1243                           fill_value=self.fill_value)1244    def mean(self, axis=0, *args, **kwargs):1245        """1246        Mean of non-NA/null values1247        Returns1248        -------1249        mean : float1250        """1251        nv.validate_mean(args, kwargs)1252        valid_vals = self._valid_sp_values1253        sp_sum = valid_vals.sum()1254        ct = len(valid_vals)1255        if self._null_fill_value:1256            return sp_sum / ct1257        else:1258            nsparse = self.sp_index.ngaps1259            return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)1260    def transpose(self, *axes):1261        """1262        Returns the SparseArray.1263        """1264        return self1265    @property1266    def T(self):1267        """1268        Returns the SparseArray.1269        """1270        return self1271    # ------------------------------------------------------------------------1272    # Ufuncs1273    # ------------------------------------------------------------------------1274    def __array_wrap__(self, array, context=None):1275        from pandas.core.dtypes.generic import ABCSparseSeries1276        ufunc, inputs, _ = context1277        inputs = tuple(x.values if isinstance(x, ABCSparseSeries) else x1278                       for x in inputs)1279        return self.__array_ufunc__(ufunc, '__call__', *inputs)1280    _HANDLED_TYPES = (np.ndarray, numbers.Number)1281    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):1282        out = kwargs.get('out', ())1283        for x in inputs + out:1284            if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):1285                return NotImplemented1286        special = {'add', 'sub', 'mul', 'pow', 'mod', 'floordiv', 'truediv',1287                   'divmod', 'eq', 'ne', 'lt', 'gt', 'le', 'ge', 'remainder'}1288        if compat.PY2:1289            special.add('div')1290        aliases = {1291            'subtract': 'sub',1292            'multiply': 'mul',1293            'floor_divide': 'floordiv',1294            'true_divide': 'truediv',1295            'power': 'pow',1296            'remainder': 'mod',1297            'divide': 'div',1298            'equal': 'eq',1299            'not_equal': 'ne',1300            'less': 'lt',1301            'less_equal': 'le',1302            'greater': 'gt',1303            'greater_equal': 'ge',1304        }1305        flipped = {1306            'lt': '__gt__',1307            'le': '__ge__',1308            'gt': '__lt__',1309            'ge': '__le__',1310            'eq': '__eq__',1311            'ne': '__ne__',1312        }1313        op_name = ufunc.__name__1314        op_name = aliases.get(op_name, op_name)1315        if op_name in special and kwargs.get('out') is None:1316            if isinstance(inputs[0], type(self)):1317                return getattr(self, '__{}__'.format(op_name))(inputs[1])1318            else:1319                name = flipped.get(op_name, '__r{}__'.format(op_name))1320                return getattr(self, name)(inputs[0])1321        if len(inputs) == 1:1322            # No alignment necessary.1323            sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)1324            fill_value = getattr(ufunc, method)(self.fill_value, **kwargs)1325            return self._simple_new(sp_values,1326                                    self.sp_index,1327                                    SparseDtype(sp_values.dtype, fill_value))1328        result = getattr(ufunc, method)(*[np.asarray(x) for x in inputs],1329                                        **kwargs)1330        if out:1331            if len(out) == 1:1332                out = out[0]1333            return out1334        if type(result) is tuple:1335            return tuple(type(self)(x) for x in result)1336        elif method == 'at':1337            # no return value1338            return None1339        else:1340            return type(self)(result)1341    def __abs__(self):1342        return np.abs(self)1343    # ------------------------------------------------------------------------1344    # Ops1345    # ------------------------------------------------------------------------1346    @classmethod1347    def _create_unary_method(cls, op):1348        def sparse_unary_method(self):1349            fill_value = op(np.array(self.fill_value)).item()1350            values = op(self.sp_values)1351            dtype = SparseDtype(values.dtype, fill_value)1352            return cls._simple_new(values, self.sp_index, dtype)1353        name = '__{name}__'.format(name=op.__name__)1354        return compat.set_function_name(sparse_unary_method, name, cls)1355    @classmethod1356    def _create_arithmetic_method(cls, op):1357        def sparse_arithmetic_method(self, other):1358            op_name = op.__name__1359            if isinstance(other, (ABCSeries, ABCIndexClass)):1360                # Rely on pandas to dispatch to us.1361                return NotImplemented1362            if isinstance(other, SparseArray):1363                return _sparse_array_op(self, other, op, op_name)1364            elif is_scalar(other):1365                with np.errstate(all='ignore'):1366                    fill = op(_get_fill(self), np.asarray(other))1367                    result = op(self.sp_values, other)1368                if op_name == 'divmod':1369                    left, right = result1370                    lfill, rfill = fill1371                    return (_wrap_result(op_name, left, self.sp_index, lfill),1372                            _wrap_result(op_name, right, self.sp_index, rfill))1373                return _wrap_result(op_name, result, self.sp_index, fill)1374            else:1375                other = np.asarray(other)1376                with np.errstate(all='ignore'):1377                    # TODO: delete sparse stuff in core/ops.py1378                    # TODO: look into _wrap_result1379                    if len(self) != len(other):1380                        raise AssertionError(1381                            ("length mismatch: {self} vs. {other}".format(1382                                self=len(self), other=len(other))))1383                    if not isinstance(other, SparseArray):1384                        dtype = getattr(other, 'dtype', None)1385                        other = SparseArray(other, fill_value=self.fill_value,1386                                            dtype=dtype)1387                    return _sparse_array_op(self, other, op, op_name)1388        name = '__{name}__'.format(name=op.__name__)1389        return compat.set_function_name(sparse_arithmetic_method, name, cls)1390    @classmethod1391    def _create_comparison_method(cls, op):1392        def cmp_method(self, other):1393            op_name = op.__name__1394            if op_name in {'and_', 'or_'}:1395                op_name = op_name[:-1]1396            if isinstance(other, (ABCSeries, ABCIndexClass)):1397                # Rely on pandas to unbox and dispatch to us.1398                return NotImplemented1399            if not is_scalar(other) and not isinstance(other, type(self)):1400                # convert list-like to ndarray1401                other = np.asarray(other)1402            if isinstance(other, np.ndarray):1403                # TODO: make this more flexible than just ndarray...1404                if len(self) != len(other):1405                    raise AssertionError("length mismatch: {self} vs. {other}"1406                                         .format(self=len(self),1407                                                 other=len(other)))1408                other = SparseArray(other, fill_value=self.fill_value)1409            if isinstance(other, SparseArray):1410                return _sparse_array_op(self, other, op, op_name)1411            else:1412                with np.errstate(all='ignore'):1413                    fill_value = op(self.fill_value, other)1414                    result = op(self.sp_values, other)1415                return type(self)(result,1416                                  sparse_index=self.sp_index,1417                                  fill_value=fill_value,1418                                  dtype=np.bool_)1419        name = '__{name}__'.format(name=op.__name__)1420        return compat.set_function_name(cmp_method, name, cls)1421    @classmethod1422    def _add_unary_ops(cls):1423        cls.__pos__ = cls._create_unary_method(operator.pos)1424        cls.__neg__ = cls._create_unary_method(operator.neg)1425        cls.__invert__ = cls._create_unary_method(operator.invert)1426    @classmethod1427    def _add_comparison_ops(cls):1428        cls.__and__ = cls._create_comparison_method(operator.and_)1429        cls.__or__ = cls._create_comparison_method(operator.or_)1430        super(SparseArray, cls)._add_comparison_ops()1431    # ----------1432    # Formatting1433    # -----------1434    def __unicode__(self):1435        return '{self}\nFill: {fill}\n{index}'.format(1436            self=printing.pprint_thing(self),1437            fill=printing.pprint_thing(self.fill_value),1438            index=printing.pprint_thing(self.sp_index))1439    def _formatter(self, boxed=False):1440        # Defer to the formatter from the GenericArrayFormatter calling us.1441        # This will infer the correct formatter from the dtype of the values.1442        return None1443SparseArray._add_arithmetic_ops()1444SparseArray._add_comparison_ops()1445SparseArray._add_unary_ops()1446def _maybe_to_dense(obj):1447    """1448    try to convert to dense1449    """1450    if hasattr(obj, 'to_dense'):1451        return obj.to_dense()1452    return obj1453def _maybe_to_sparse(array):1454    """1455    array must be SparseSeries or SparseArray1456    """1457    if isinstance(array, ABCSparseSeries):1458        array = array.values.copy()1459    return array1460def _sanitize_values(arr):1461    """1462    return an ndarray for our input,1463    in a platform independent manner1464    """1465    if hasattr(arr, 'values'):1466        arr = arr.values1467    else:1468        # scalar1469        if is_scalar(arr):1470            arr = [arr]1471        # ndarray1472        if isinstance(arr, np.ndarray):1473            pass1474        elif is_list_like(arr) and len(arr) > 0:1475            arr = maybe_convert_platform(arr)1476        else:1477            arr = np.asarray(arr)1478    return arr1479def make_sparse(arr, kind='block', fill_value=None, dtype=None, copy=False):1480    """1481    Convert ndarray to sparse format1482    Parameters1483    ----------1484    arr : ndarray1485    kind : {'block', 'integer'}1486    fill_value : NaN or another value1487    dtype : np.dtype, optional1488    copy : bool, default False1489    Returns1490    -------1491    (sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)1492    """1493    arr = _sanitize_values(arr)1494    if arr.ndim > 1:1495        raise TypeError("expected dimension <= 1 data")1496    if fill_value is None:1497        fill_value = na_value_for_dtype(arr.dtype)1498    if isna(fill_value):1499        mask = notna(arr)1500    else:1501        # For str arrays in NumPy 1.12.0, operator!= below isn't1502        # element-wise but just returns False if fill_value is not str,1503        # so cast to object comparison to be safe1504        if is_string_dtype(arr):1505            arr = arr.astype(object)1506        if is_object_dtype(arr.dtype):1507            # element-wise equality check method in numpy doesn't treat1508            # each element type, eg. 0, 0.0, and False are treated as1509            # same. So we have to check the both of its type and value.1510            mask = splib.make_mask_object_ndarray(arr, fill_value)1511        else:1512            mask = arr != fill_value1513    length = len(arr)1514    if length != len(mask):1515        # the arr is a SparseArray1516        indices = mask.sp_index.indices1517    else:1518        indices = mask.nonzero()[0].astype(np.int32)1519    index = _make_index(length, indices, kind)1520    sparsified_values = arr[mask]1521    if dtype is not None:1522        sparsified_values = astype_nansafe(sparsified_values, dtype=dtype)1523    # TODO: copy1524    return sparsified_values, index, fill_value1525def _make_index(length, indices, kind):1526    if kind == 'block' or isinstance(kind, BlockIndex):1527        locs, lens = splib.get_blocks(indices)1528        index = BlockIndex(length, locs, lens)1529    elif kind == 'integer' or isinstance(kind, IntIndex):1530        index = IntIndex(length, indices)1531    else:  # pragma: no cover1532        raise ValueError('must be block or integer type')1533    return index1534# ----------------------------------------------------------------------------1535# Accessor1536@delegate_names(SparseArray, ['npoints', 'density', 'fill_value',1537                              'sp_values'],1538                typ='property')1539class SparseAccessor(PandasDelegate):1540    """1541    Accessor for SparseSparse from other sparse matrix data types.1542    """1543    def __init__(self, data=None):1544        self._validate(data)1545        # Store the Series since we need that for to_coo1546        self._parent = data1547    @staticmethod1548    def _validate(data):1549        if not isinstance(data.dtype, SparseDtype):1550            msg = "Can only use the '.sparse' accessor with Sparse data."1551            raise AttributeError(msg)1552    def _delegate_property_get(self, name, *args, **kwargs):1553        return getattr(self._parent.values, name)1554    def _delegate_method(self, name, *args, **kwargs):1555        if name == 'from_coo':1556            return self.from_coo(*args, **kwargs)1557        elif name == 'to_coo':1558            return self.to_coo(*args, **kwargs)1559        else:1560            raise ValueError1561    @classmethod1562    def from_coo(cls, A, dense_index=False):1563        """1564        Create a SparseSeries from a scipy.sparse.coo_matrix.1565        Parameters1566        ----------1567        A : scipy.sparse.coo_matrix1568        dense_index : bool, default False1569            If False (default), the SparseSeries index consists of only the1570            coords of the non-null entries of the original coo_matrix.1571            If True, the SparseSeries index consists of the full sorted1572            (row, col) coordinates of the coo_matrix.1573        Returns1574        -------1575        s : SparseSeries1576        Examples1577        ---------1578        >>> from scipy import sparse1579        >>> A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),1580                               shape=(3, 4))1581        >>> A1582        <3x4 sparse matrix of type '<class 'numpy.float64'>'1583                with 3 stored elements in COOrdinate format>1584        >>> A.todense()1585        matrix([[ 0.,  0.,  1.,  2.],1586                [ 3.,  0.,  0.,  0.],1587                [ 0.,  0.,  0.,  0.]])1588        >>> ss = pd.SparseSeries.from_coo(A)1589        >>> ss1590        0  2    11591           3    21592        1  0    31593        dtype: float641594        BlockIndex1595        Block locations: array([0], dtype=int32)1596        Block lengths: array([3], dtype=int32)1597        """1598        from pandas.core.sparse.scipy_sparse import _coo_to_sparse_series1599        from pandas import Series1600        result = _coo_to_sparse_series(A, dense_index=dense_index)1601        # SparseSeries -> Series[sparse]1602        result = Series(result.values, index=result.index, copy=False)1603        return result1604    def to_coo(self, row_levels=(0, ), column_levels=(1, ), sort_labels=False):1605        """1606        Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex.1607        Use row_levels and column_levels to determine the row and column1608        coordinates respectively. row_levels and column_levels are the names1609        (labels) or numbers of the levels. {row_levels, column_levels} must be1610        a partition of the MultiIndex level names (or numbers).1611        Parameters1612        ----------1613        row_levels : tuple/list1614        column_levels : tuple/list1615        sort_labels : bool, default False1616            Sort the row and column labels before forming the sparse matrix.1617        Returns1618        -------1619        y : scipy.sparse.coo_matrix1620        rows : list (row labels)1621        columns : list (column labels)1622        Examples1623        --------1624        >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])1625        >>> s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),1626                                                (1, 2, 'a', 1),1627                                                (1, 1, 'b', 0),1628                                                (1, 1, 'b', 1),1629                                                (2, 1, 'b', 0),1630                                                (2, 1, 'b', 1)],1631                                                names=['A', 'B', 'C', 'D'])1632        >>> ss = s.to_sparse()1633        >>> A, rows, columns = ss.to_coo(row_levels=['A', 'B'],1634                                         column_levels=['C', 'D'],1635                                         sort_labels=True)1636        >>> A1637        <3x4 sparse matrix of type '<class 'numpy.float64'>'1638                with 3 stored elements in COOrdinate format>1639        >>> A.todense()1640        matrix([[ 0.,  0.,  1.,  3.],1641        [ 3.,  0.,  0.,  0.],1642        [ 0.,  0.,  0.,  0.]])1643        >>> rows1644        [(1, 1), (1, 2), (2, 1)]1645        >>> columns1646        [('a', 0), ('a', 1), ('b', 0), ('b', 1)]1647        """1648        from pandas.core.sparse.scipy_sparse import _sparse_series_to_coo1649        A, rows, columns = _sparse_series_to_coo(self._parent,1650                                                 row_levels,1651                                                 column_levels,1652                                                 sort_labels=sort_labels)...

0002_auto_20210108_1807.py

Source:0002_auto_20210108_1807.py

1# Generated by Django 3.1.4 on 2021-01-08 18:072from django.db import migrations, models3class Migration(migrations.Migration):4    dependencies = [5        ('module', '0001_initial'),6    ]7    operations = [8        migrations.RemoveField(9            model_name='module',10            name='icon',11        ),12        migrations.AddField(13            model_name='module',14            name='icon_name',15            field=models.CharField(blank=True, choices=[('house-door.svg', 'house-door.svg'), ('graph-up.svg', 'graph-up.svg'), ('sort-alpha-up-alt.svg', 'sort-alpha-up-alt.svg'), ('eye-slash-fill.svg', 'eye-slash-fill.svg'), ('arrows-angle-expand.svg', 'arrows-angle-expand.svg'), ('layout-text-window.svg', 'layout-text-window.svg'), ('textarea.svg', 'textarea.svg'), ('slack.svg', 'slack.svg'), ('tv-fill.svg', 'tv-fill.svg'), ('hash.svg', 'hash.svg'), ('file-medical-fill.svg', 'file-medical-fill.svg'), ('calculator-fill.svg', 'calculator-fill.svg'), ('twitter.svg', 'twitter.svg'), ('bookmark-heart.svg', 'bookmark-heart.svg'), ('star.svg', 'star.svg'), ('at.svg', 'at.svg'), ('type-italic.svg', 'type-italic.svg'), ('hand-index.svg', 'hand-index.svg'), ('volume-off.svg', 'volume-off.svg'), ('telephone-plus-fill.svg', 'telephone-plus-fill.svg'), ('arrow-down-left.svg', 'arrow-down-left.svg'), ('hand-index-thumb.svg', 'hand-index-thumb.svg'), ('calendar2-range.svg', 'calendar2-range.svg'), ('dice-4.svg', 'dice-4.svg'), ('inboxes-fill.svg', 'inboxes-fill.svg'), ('bag-x-fill.svg', 'bag-x-fill.svg'), ('caret-down-square-fill.svg', 'caret-down-square-fill.svg'), ('thermometer-half.svg', 'thermometer-half.svg'), ('calendar2-day-fill.svg', 'calendar2-day-fill.svg'), ('alt.svg', 'alt.svg'), ('heart-half.svg', 'heart-half.svg'), ('pause-btn.svg', 'pause-btn.svg'), ('plus-circle-fill.svg', 'plus-circle-fill.svg'), ('mic.svg', 'mic.svg'), ('person-lines-fill.svg', 'person-lines-fill.svg'), ('markdown.svg', 'markdown.svg'), ('shield-minus.svg', 'shield-minus.svg'), ('file-earmark-richtext-fill.svg', 'file-earmark-richtext-fill.svg'), ('trophy-fill.svg', 'trophy-fill.svg'), ('hdd.svg', 'hdd.svg'), ('shop.svg', 'shop.svg'), ('skip-backward-circle.svg', 'skip-backward-circle.svg'), ('bar-chart.svg', 'bar-chart.svg'), ('phone-landscape.svg', 'phone-landscape.svg'), ('suit-spade.svg', 'suit-spade.svg'), ('wrench.svg', 'wrench.svg'), ('badge-vo.svg', 'badge-vo.svg'), ('house-door-fill.svg', 'house-door-fill.svg'), ('sort-down.svg', 'sort-down.svg'), ('plus-circle.svg', 'plus-circle.svg'), ('skip-end-circle.svg', 'skip-end-circle.svg'), ('inboxes.svg', 'inboxes.svg'), ('envelope-open-fill.svg', 'envelope-open-fill.svg'), ('file-arrow-down.svg', 'file-arrow-down.svg'), ('file-x.svg', 'file-x.svg'), ('record-btn.svg', 'record-btn.svg'), ('basket.svg', 'basket.svg'), ('award-fill.svg', 'award-fill.svg'), ('badge-8k.svg', 'badge-8k.svg'), ('flower3.svg', 'flower3.svg'), ('skip-start-circle-fill.svg', 'skip-start-circle-fill.svg'), ('voicemail.svg', 'voicemail.svg'), ('arrow-up-down.svg', 'arrow-up-down.svg'), ('ui-checks-grid.svg', 'ui-checks-grid.svg'), ('upc.svg', 'upc.svg'), ('tablet.svg', 'tablet.svg'), ('bag-plus.svg', 'bag-plus.svg'), ('calendar3.svg', 'calendar3.svg'), ('calendar2-x-fill.svg', 'calendar2-x-fill.svg'), ('arrow-up-left-circle.svg', 'arrow-up-left-circle.svg'), ('chat-square-text.svg', 'chat-square-text.svg'), ('toggle-on.svg', 'toggle-on.svg'), ('patch-plus.svg', 'patch-plus.svg'), ('peace.svg', 'peace.svg'), ('file-word.svg', 'file-word.svg'), ('chat-right-quote.svg', 'chat-right-quote.svg'), ('file-earmark-person.svg', 'file-earmark-person.svg'), ('bell.svg', 'bell.svg'), ('share.svg', 'share.svg'), ('easel.svg', 'easel.svg'), ('layout-text-window-reverse.svg', 'layout-text-window-reverse.svg'), ('file-bar-graph-fill.svg', 'file-bar-graph-fill.svg'), ('pentagon.svg', 'pentagon.svg'), ('table.svg', 'table.svg'), ('patch-question.svg', 'patch-question.svg'), ('gem.svg', 'gem.svg'), ('person-fill.svg', 'person-fill.svg'), ('file-earmark-excel-fill.svg', 'file-earmark-excel-fill.svg'), ('bookmark-star-fill.svg', 'bookmark-star-fill.svg'), ('option.svg', 'option.svg'), ('stop-circle.svg', 'stop-circle.svg'), ('layout-sidebar-inset-reverse.svg', 'layout-sidebar-inset-reverse.svg'), ('bag-check.svg', 'bag-check.svg'), ('badge-cc.svg', 'badge-cc.svg'), ('octagon-fill.svg', 'octagon-fill.svg'), ('pause-circle.svg', 'pause-circle.svg'), ('arrow-down-square.svg', 'arrow-down-square.svg'), ('hdd-network-fill.svg', 'hdd-network-fill.svg'), ('chevron-right.svg', 'chevron-right.svg'), ('peace-fill.svg', 'peace-fill.svg'), ('journal-bookmark-fill.svg', 'journal-bookmark-fill.svg'), ('collection.svg', 'collection.svg'), ('headphones.svg', 'headphones.svg'), ('chat-square.svg', 'chat-square.svg'), ('filter-square.svg', 'filter-square.svg'), ('blockquote-left.svg', 'blockquote-left.svg'), ('folder-fill.svg', 'folder-fill.svg'), ('crop.svg', 'crop.svg'), ('play-circle.svg', 'play-circle.svg'), 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'check-box.svg'), ('kanban-fill.svg', 'kanban-fill.svg'), ('camera-fill.svg', 'camera-fill.svg'), ('badge-hd.svg', 'badge-hd.svg'), ('stickies.svg', 'stickies.svg'), ('vector-pen.svg', 'vector-pen.svg'), ('map.svg', 'map.svg'), ('layers-half.svg', 'layers-half.svg'), ('pencil-square.svg', 'pencil-square.svg'), ('file-earmark-zip-fill.svg', 'file-earmark-zip-fill.svg'), ('pause-fill.svg', 'pause-fill.svg'), ('reply-fill.svg', 'reply-fill.svg'), ('toggles2.svg', 'toggles2.svg'), ('fullscreen.svg', 'fullscreen.svg'), ('music-note.svg', 'music-note.svg'), ('journal-text.svg', 'journal-text.svg'), ('x-diamond.svg', 'x-diamond.svg'), ('node-minus-fill.svg', 'node-minus-fill.svg'), ('file-arrow-down-fill.svg', 'file-arrow-down-fill.svg'), ('laptop-fill.svg', 'laptop-fill.svg'), ('chevron-double-down.svg', 'chevron-double-down.svg'), ('heart-fill.svg', 'heart-fill.svg'), ('skip-end-btn-fill.svg', 'skip-end-btn-fill.svg'), ('record2.svg', 'record2.svg'), ('chevron-double-left.svg', 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0003_auto_20210110_1957.py

Source:0003_auto_20210110_1957.py

1# Generated by Django 3.1.4 on 2021-01-10 19:572from django.db import migrations, models3class Migration(migrations.Migration):4    dependencies = [5        ('module', '0002_auto_20210108_1807'),6    ]7    operations = [8        migrations.AlterField(9            model_name='module',10            name='icon_name',11            field=models.CharField(blank=True, choices=[('alarm-fill.svg', 'alarm-fill.svg'), ('alarm.svg', 'alarm.svg'), ('align-bottom.svg', 'align-bottom.svg'), ('align-center.svg', 'align-center.svg'), ('align-end.svg', 'align-end.svg'), ('align-middle.svg', 'align-middle.svg'), ('align-start.svg', 'align-start.svg'), ('align-top.svg', 'align-top.svg'), ('alt.svg', 'alt.svg'), ('app-indicator.svg', 'app-indicator.svg'), ('app.svg', 'app.svg'), ('archive-fill.svg', 'archive-fill.svg'), ('archive.svg', 'archive.svg'), ('arrow-90deg-down.svg', 'arrow-90deg-down.svg'), ('arrow-90deg-left.svg', 'arrow-90deg-left.svg'), ('arrow-90deg-right.svg', 'arrow-90deg-right.svg'), 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'arrow-up-short.svg'), ('arrow-up-square-fill.svg', 'arrow-up-square-fill.svg'), ('arrow-up-square.svg', 'arrow-up-square.svg'), ('arrow-up.svg', 'arrow-up.svg'), ('arrows-angle-contract.svg', 'arrows-angle-contract.svg'), ('arrows-angle-expand.svg', 'arrows-angle-expand.svg'), ('arrows-collapse.svg', 'arrows-collapse.svg'), ('arrows-expand.svg', 'arrows-expand.svg'), ('arrows-fullscreen.svg', 'arrows-fullscreen.svg'), ('arrows-move.svg', 'arrows-move.svg'), ('aspect-ratio-fill.svg', 'aspect-ratio-fill.svg'), ('aspect-ratio.svg', 'aspect-ratio.svg'), ('asterisk.svg', 'asterisk.svg'), ('at.svg', 'at.svg'), ('award-fill.svg', 'award-fill.svg'), ('award.svg', 'award.svg'), ('back.svg', 'back.svg'), ('backspace-fill.svg', 'backspace-fill.svg'), ('backspace-reverse-fill.svg', 'backspace-reverse-fill.svg'), ('backspace-reverse.svg', 'backspace-reverse.svg'), ('backspace.svg', 'backspace.svg'), ('badge-4k-fill.svg', 'badge-4k-fill.svg'), ('badge-4k.svg', 'badge-4k.svg'), ('badge-8k-fill.svg', 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'trophy-fill.svg'), ('trophy.svg', 'trophy.svg'), ('truck-flatbed.svg', 'truck-flatbed.svg'), ('truck.svg', 'truck.svg'), ('tv-fill.svg', 'tv-fill.svg'), ('tv.svg', 'tv.svg'), ('twitch.svg', 'twitch.svg'), ('twitter.svg', 'twitter.svg'), ('type-bold.svg', 'type-bold.svg'), ('type-h1.svg', 'type-h1.svg'), ('type-h2.svg', 'type-h2.svg'), ('type-h3.svg', 'type-h3.svg'), ('type-italic.svg', 'type-italic.svg'), ('type-strikethrough.svg', 'type-strikethrough.svg'), ('type-underline.svg', 'type-underline.svg'), ('type.svg', 'type.svg'), ('ui-checks-grid.svg', 'ui-checks-grid.svg'), ('ui-checks.svg', 'ui-checks.svg'), ('ui-radios-grid.svg', 'ui-radios-grid.svg'), ('ui-radios.svg', 'ui-radios.svg'), ('union.svg', 'union.svg'), ('unlock-fill.svg', 'unlock-fill.svg'), ('unlock.svg', 'unlock.svg'), ('upc-scan.svg', 'upc-scan.svg'), ('upc.svg', 'upc.svg'), ('upload.svg', 'upload.svg'), ('vector-pen.svg', 'vector-pen.svg'), ('view-list.svg', 'view-list.svg'), ('view-stacked.svg', 'view-stacked.svg'), ('vinyl-fill.svg', 'vinyl-fill.svg'), ('vinyl.svg', 'vinyl.svg'), ('voicemail.svg', 'voicemail.svg'), ('volume-down-fill.svg', 'volume-down-fill.svg'), ('volume-down.svg', 'volume-down.svg'), ('volume-mute-fill.svg', 'volume-mute-fill.svg'), ('volume-mute.svg', 'volume-mute.svg'), ('volume-off-fill.svg', 'volume-off-fill.svg'), ('volume-off.svg', 'volume-off.svg'), ('volume-up-fill.svg', 'volume-up-fill.svg'), ('volume-up.svg', 'volume-up.svg'), ('vr.svg', 'vr.svg'), ('wallet-fill.svg', 'wallet-fill.svg'), ('wallet.svg', 'wallet.svg'), ('wallet2.svg', 'wallet2.svg'), ('watch.svg', 'watch.svg'), ('wifi-1.svg', 'wifi-1.svg'), ('wifi-2.svg', 'wifi-2.svg'), ('wifi-off.svg', 'wifi-off.svg'), ('wifi.svg', 'wifi.svg'), ('window.svg', 'window.svg'), ('wrench.svg', 'wrench.svg'), ('x-circle-fill.svg', 'x-circle-fill.svg'), ('x-circle.svg', 'x-circle.svg'), ('x-diamond-fill.svg', 'x-diamond-fill.svg'), ('x-diamond.svg', 'x-diamond.svg'), ('x-octagon-fill.svg', 'x-octagon-fill.svg'), ('x-octagon.svg', 'x-octagon.svg'), ('x-square-fill.svg', 'x-square-fill.svg'), ('x-square.svg', 'x-square.svg'), ('x.svg', 'x.svg'), ('youtube.svg', 'youtube.svg'), ('zoom-in.svg', 'zoom-in.svg'), ('zoom-out.svg', 'zoom-out.svg')], max_length=100, verbose_name='Ãcono'),12        ),...

test_array.py

Source:test_array.py

1import operator2import re3import warnings4import numpy as np5import pytest6from pandas._libs.sparse import IntIndex7from pandas.compat import range8import pandas.util._test_decorators as td9import pandas as pd10from pandas import isna11from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries12import pandas.util.testing as tm13from pandas.util.testing import assert_almost_equal14@pytest.fixture(params=["integer", "block"])15def kind(request):16    return request.param17class TestSparseArray(object):18    def setup_method(self, method):19        self.arr_data = np.array([np.nan, np.nan, 1, 2, 3,20                                  np.nan, 4, 5, np.nan, 6])21        self.arr = SparseArray(self.arr_data)22        self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)23    def test_constructor_dtype(self):24        arr = SparseArray([np.nan, 1, 2, np.nan])25        assert arr.dtype == SparseDtype(np.float64, np.nan)26        assert arr.dtype.subtype == np.float6427        assert np.isnan(arr.fill_value)28        arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0)29        assert arr.dtype == SparseDtype(np.float64, 0)30        assert arr.fill_value == 031        arr = SparseArray([0, 1, 2, 4], dtype=np.float64)32        assert arr.dtype == SparseDtype(np.float64, np.nan)33        assert np.isnan(arr.fill_value)34        arr = SparseArray([0, 1, 2, 4], dtype=np.int64)35        assert arr.dtype == SparseDtype(np.int64, 0)36        assert arr.fill_value == 037        arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64)38        assert arr.dtype == SparseDtype(np.int64, 0)39        assert arr.fill_value == 040        arr = SparseArray([0, 1, 2, 4], dtype=None)41        assert arr.dtype == SparseDtype(np.int64, 0)42        assert arr.fill_value == 043        arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None)44        assert arr.dtype == SparseDtype(np.int64, 0)45        assert arr.fill_value == 046    def test_constructor_dtype_str(self):47        result = SparseArray([1, 2, 3], dtype='int')48        expected = SparseArray([1, 2, 3], dtype=int)49        tm.assert_sp_array_equal(result, expected)50    def test_constructor_sparse_dtype(self):51        result = SparseArray([1, 0, 0, 1], dtype=SparseDtype('int64', -1))52        expected = SparseArray([1, 0, 0, 1], fill_value=-1, dtype=np.int64)53        tm.assert_sp_array_equal(result, expected)54        assert result.sp_values.dtype == np.dtype('int64')55    def test_constructor_sparse_dtype_str(self):56        result = SparseArray([1, 0, 0, 1], dtype='Sparse[int32]')57        expected = SparseArray([1, 0, 0, 1], dtype=np.int32)58        tm.assert_sp_array_equal(result, expected)59        assert result.sp_values.dtype == np.dtype('int32')60    def test_constructor_object_dtype(self):61        # GH 1185662        arr = SparseArray(['A', 'A', np.nan, 'B'], dtype=np.object)63        assert arr.dtype == SparseDtype(np.object)64        assert np.isnan(arr.fill_value)65        arr = SparseArray(['A', 'A', np.nan, 'B'], dtype=np.object,66                          fill_value='A')67        assert arr.dtype == SparseDtype(np.object, 'A')68        assert arr.fill_value == 'A'69        # GH 1757470        data = [False, 0, 100.0, 0.0]71        arr = SparseArray(data, dtype=np.object, fill_value=False)72        assert arr.dtype == SparseDtype(np.object, False)73        assert arr.fill_value is False74        arr_expected = np.array(data, dtype=np.object)75        it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))76        assert np.fromiter(it, dtype=np.bool).all()77    @pytest.mark.parametrize("dtype", [SparseDtype(int, 0), int])78    def test_constructor_na_dtype(self, dtype):79        with pytest.raises(ValueError, match="Cannot convert"):80            SparseArray([0, 1, np.nan], dtype=dtype)81    def test_constructor_spindex_dtype(self):82        arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))83        # XXX: Behavior change: specifying SparseIndex no longer changes the84        # fill_value85        expected = SparseArray([0, 1, 2, 0], kind='integer')86        tm.assert_sp_array_equal(arr, expected)87        assert arr.dtype == SparseDtype(np.int64)88        assert arr.fill_value == 089        arr = SparseArray(data=[1, 2, 3],90                          sparse_index=IntIndex(4, [1, 2, 3]),91                          dtype=np.int64, fill_value=0)92        exp = SparseArray([0, 1, 2, 3], dtype=np.int64, fill_value=0)93        tm.assert_sp_array_equal(arr, exp)94        assert arr.dtype == SparseDtype(np.int64)95        assert arr.fill_value == 096        arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]),97                          fill_value=0, dtype=np.int64)98        exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64)99        tm.assert_sp_array_equal(arr, exp)100        assert arr.dtype == SparseDtype(np.int64)101        assert arr.fill_value == 0102        arr = SparseArray(data=[1, 2, 3],103                          sparse_index=IntIndex(4, [1, 2, 3]),104                          dtype=None, fill_value=0)105        exp = SparseArray([0, 1, 2, 3], dtype=None)106        tm.assert_sp_array_equal(arr, exp)107        assert arr.dtype == SparseDtype(np.int64)108        assert arr.fill_value == 0109    @pytest.mark.parametrize("sparse_index", [110        None, IntIndex(1, [0]),111    ])112    def test_constructor_spindex_dtype_scalar(self, sparse_index):113        # scalar input114        arr = SparseArray(data=1, sparse_index=sparse_index, dtype=None)115        exp = SparseArray([1], dtype=None)116        tm.assert_sp_array_equal(arr, exp)117        assert arr.dtype == SparseDtype(np.int64)118        assert arr.fill_value == 0119        arr = SparseArray(data=1, sparse_index=IntIndex(1, [0]), dtype=None)120        exp = SparseArray([1], dtype=None)121        tm.assert_sp_array_equal(arr, exp)122        assert arr.dtype == SparseDtype(np.int64)123        assert arr.fill_value == 0124    def test_constructor_spindex_dtype_scalar_broadcasts(self):125        arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]),126                          fill_value=0, dtype=None)127        exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None)128        tm.assert_sp_array_equal(arr, exp)129        assert arr.dtype == SparseDtype(np.int64)130        assert arr.fill_value == 0131    @pytest.mark.parametrize('data, fill_value', [132        (np.array([1, 2]), 0),133        (np.array([1.0, 2.0]), np.nan),134        ([True, False], False),135        ([pd.Timestamp('2017-01-01')], pd.NaT),136    ])137    def test_constructor_inferred_fill_value(self, data, fill_value):138        result = SparseArray(data).fill_value139        if pd.isna(fill_value):140            assert pd.isna(result)141        else:142            assert result == fill_value143    @pytest.mark.parametrize('scalar,dtype', [144        (False, SparseDtype(bool, False)),145        (0.0, SparseDtype('float64', 0)),146        (1, SparseDtype('int64', 1)),147        ('z', SparseDtype('object', 'z'))])148    def test_scalar_with_index_infer_dtype(self, scalar, dtype):149        # GH 19163150        arr = SparseArray(scalar, index=[1, 2, 3], fill_value=scalar)151        exp = SparseArray([scalar, scalar, scalar], fill_value=scalar)152        tm.assert_sp_array_equal(arr, exp)153        assert arr.dtype == dtype154        assert exp.dtype == dtype155    @pytest.mark.parametrize("fill", [1, np.nan, 0])156    def test_sparse_series_round_trip(self, kind, fill):157        # see gh-13999158        arr = SparseArray([np.nan, 1, np.nan, 2, 3],159                          kind=kind, fill_value=fill)160        res = SparseArray(SparseSeries(arr))161        tm.assert_sp_array_equal(arr, res)162        arr = SparseArray([0, 0, 0, 1, 1, 2], dtype=np.int64,163                          kind=kind, fill_value=fill)164        res = SparseArray(SparseSeries(arr), dtype=np.int64)165        tm.assert_sp_array_equal(arr, res)166        res = SparseArray(SparseSeries(arr))167        tm.assert_sp_array_equal(arr, res)168    @pytest.mark.parametrize("fill", [True, False, np.nan])169    def test_sparse_series_round_trip2(self, kind, fill):170        # see gh-13999171        arr = SparseArray([True, False, True, True], dtype=np.bool,172                          kind=kind, fill_value=fill)173        res = SparseArray(SparseSeries(arr))174        tm.assert_sp_array_equal(arr, res)175        res = SparseArray(SparseSeries(arr))176        tm.assert_sp_array_equal(arr, res)177    def test_get_item(self):178        assert np.isnan(self.arr[1])179        assert self.arr[2] == 1180        assert self.arr[7] == 5181        assert self.zarr[0] == 0182        assert self.zarr[2] == 1183        assert self.zarr[7] == 5184        errmsg = re.compile("bounds")185        with pytest.raises(IndexError, match=errmsg):186            self.arr[11]187        with pytest.raises(IndexError, match=errmsg):188            self.arr[-11]189        assert self.arr[-1] == self.arr[len(self.arr) - 1]190    def test_take_scalar_raises(self):191        msg = "'indices' must be an array, not a scalar '2'."192        with pytest.raises(ValueError, match=msg):193            self.arr.take(2)194    def test_take(self):195        exp = SparseArray(np.take(self.arr_data, [2, 3]))196        tm.assert_sp_array_equal(self.arr.take([2, 3]), exp)197        exp = SparseArray(np.take(self.arr_data, [0, 1, 2]))198        tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp)199    def test_take_fill_value(self):200        data = np.array([1, np.nan, 0, 3, 0])201        sparse = SparseArray(data, fill_value=0)202        exp = SparseArray(np.take(data, [0]), fill_value=0)203        tm.assert_sp_array_equal(sparse.take([0]), exp)204        exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0)205        tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp)206    def test_take_negative(self):207        exp = SparseArray(np.take(self.arr_data, [-1]))208        tm.assert_sp_array_equal(self.arr.take([-1]), exp)209        exp = SparseArray(np.take(self.arr_data, [-4, -3, -2]))210        tm.assert_sp_array_equal(self.arr.take([-4, -3, -2]), exp)211    @pytest.mark.parametrize('fill_value', [0, None, np.nan])212    def test_shift_fill_value(self, fill_value):213        # GH #24128214        sparse = SparseArray(np.array([1, 0, 0, 3, 0]),215                             fill_value=8.0)216        res = sparse.shift(1, fill_value=fill_value)217        if isna(fill_value):218            fill_value = res.dtype.na_value219        exp = SparseArray(np.array([fill_value, 1, 0, 0, 3]),220                          fill_value=8.0)221        tm.assert_sp_array_equal(res, exp)222    def test_bad_take(self):223        with pytest.raises(IndexError, match="bounds"):224            self.arr.take([11])225    def test_take_filling(self):226        # similar tests as GH 12631227        sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4])228        result = sparse.take(np.array([1, 0, -1]))229        expected = SparseArray([np.nan, np.nan, 4])230        tm.assert_sp_array_equal(result, expected)231        # XXX: test change: fill_value=True -> allow_fill=True232        result = sparse.take(np.array([1, 0, -1]), allow_fill=True)233        expected = SparseArray([np.nan, np.nan, np.nan])234        tm.assert_sp_array_equal(result, expected)235        # allow_fill=False236        result = sparse.take(np.array([1, 0, -1]),237                             allow_fill=False, fill_value=True)238        expected = SparseArray([np.nan, np.nan, 4])239        tm.assert_sp_array_equal(result, expected)240        msg = "Invalid value in 'indices'"241        with pytest.raises(ValueError, match=msg):242            sparse.take(np.array([1, 0, -2]), allow_fill=True)243        with pytest.raises(ValueError, match=msg):244            sparse.take(np.array([1, 0, -5]), allow_fill=True)245        with pytest.raises(IndexError):246            sparse.take(np.array([1, -6]))247        with pytest.raises(IndexError):248            sparse.take(np.array([1, 5]))249        with pytest.raises(IndexError):250            sparse.take(np.array([1, 5]), allow_fill=True)251    def test_take_filling_fill_value(self):252        # same tests as GH 12631253        sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0)254        result = sparse.take(np.array([1, 0, -1]))255        expected = SparseArray([0, np.nan, 4], fill_value=0)256        tm.assert_sp_array_equal(result, expected)257        # fill_value258        result = sparse.take(np.array([1, 0, -1]), allow_fill=True)259        # XXX: behavior change.260        # the old way of filling self.fill_value doesn't follow EA rules.261        # It's supposed to be self.dtype.na_value (nan in this case)262        expected = SparseArray([0, np.nan, np.nan], fill_value=0)263        tm.assert_sp_array_equal(result, expected)264        # allow_fill=False265        result = sparse.take(np.array([1, 0, -1]),266                             allow_fill=False, fill_value=True)267        expected = SparseArray([0, np.nan, 4], fill_value=0)268        tm.assert_sp_array_equal(result, expected)269        msg = ("Invalid value in 'indices'.")270        with pytest.raises(ValueError, match=msg):271            sparse.take(np.array([1, 0, -2]), allow_fill=True)272        with pytest.raises(ValueError, match=msg):273            sparse.take(np.array([1, 0, -5]), allow_fill=True)274        with pytest.raises(IndexError):275            sparse.take(np.array([1, -6]))276        with pytest.raises(IndexError):277            sparse.take(np.array([1, 5]))278        with pytest.raises(IndexError):279            sparse.take(np.array([1, 5]), fill_value=True)280    def test_take_filling_all_nan(self):281        sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan])282        # XXX: did the default kind from take change?283        result = sparse.take(np.array([1, 0, -1]))284        expected = SparseArray([np.nan, np.nan, np.nan], kind='block')285        tm.assert_sp_array_equal(result, expected)286        result = sparse.take(np.array([1, 0, -1]), fill_value=True)287        expected = SparseArray([np.nan, np.nan, np.nan], kind='block')288        tm.assert_sp_array_equal(result, expected)289        with pytest.raises(IndexError):290            sparse.take(np.array([1, -6]))291        with pytest.raises(IndexError):292            sparse.take(np.array([1, 5]))293        with pytest.raises(IndexError):294            sparse.take(np.array([1, 5]), fill_value=True)295    def test_set_item(self):296        def setitem():297            self.arr[5] = 3298        def setslice():299            self.arr[1:5] = 2300        with pytest.raises(TypeError, match="assignment via setitem"):301            setitem()302        with pytest.raises(TypeError, match="assignment via setitem"):303            setslice()304    def test_constructor_from_too_large_array(self):305        with pytest.raises(TypeError, match="expected dimension <= 1 data"):306            SparseArray(np.arange(10).reshape((2, 5)))307    def test_constructor_from_sparse(self):308        res = SparseArray(self.zarr)309        assert res.fill_value == 0310        assert_almost_equal(res.sp_values, self.zarr.sp_values)311    def test_constructor_copy(self):312        cp = SparseArray(self.arr, copy=True)313        cp.sp_values[:3] = 0314        assert not (self.arr.sp_values[:3] == 0).any()315        not_copy = SparseArray(self.arr)316        not_copy.sp_values[:3] = 0317        assert (self.arr.sp_values[:3] == 0).all()318    def test_constructor_bool(self):319        # GH 10648320        data = np.array([False, False, True, True, False, False])321        arr = SparseArray(data, fill_value=False, dtype=bool)322        assert arr.dtype == SparseDtype(bool)323        tm.assert_numpy_array_equal(arr.sp_values, np.array([True, True]))324        # Behavior change: np.asarray densifies.325        # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))326        tm.assert_numpy_array_equal(arr.sp_index.indices,327                                    np.array([2, 3], np.int32))328        for dense in [arr.to_dense(), arr.values]:329            assert dense.dtype == bool330            tm.assert_numpy_array_equal(dense, data)331    def test_constructor_bool_fill_value(self):332        arr = SparseArray([True, False, True], dtype=None)333        assert arr.dtype == SparseDtype(np.bool)334        assert not arr.fill_value335        arr = SparseArray([True, False, True], dtype=np.bool)336        assert arr.dtype == SparseDtype(np.bool)337        assert not arr.fill_value338        arr = SparseArray([True, False, True], dtype=np.bool, fill_value=True)339        assert arr.dtype == SparseDtype(np.bool, True)340        assert arr.fill_value341    def test_constructor_float32(self):342        # GH 10648343        data = np.array([1., np.nan, 3], dtype=np.float32)344        arr = SparseArray(data, dtype=np.float32)345        assert arr.dtype == SparseDtype(np.float32)346        tm.assert_numpy_array_equal(arr.sp_values,347                                    np.array([1, 3], dtype=np.float32))348        # Behavior change: np.asarray densifies.349        # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))350        tm.assert_numpy_array_equal(arr.sp_index.indices,351                                    np.array([0, 2], dtype=np.int32))352        for dense in [arr.to_dense(), arr.values]:353            assert dense.dtype == np.float32354            tm.assert_numpy_array_equal(dense, data)355    def test_astype(self):356        # float -> float357        arr = SparseArray([None, None, 0, 2])358        result = arr.astype("Sparse[float32]")359        expected = SparseArray([None, None, 0, 2], dtype=np.dtype('float32'))360        tm.assert_sp_array_equal(result, expected)361        dtype = SparseDtype("float64", fill_value=0)362        result = arr.astype(dtype)363        expected = SparseArray._simple_new(np.array([0., 2.],364                                                    dtype=dtype.subtype),365                                           IntIndex(4, [2, 3]),366                                           dtype)367        tm.assert_sp_array_equal(result, expected)368        dtype = SparseDtype("int64", 0)369        result = arr.astype(dtype)370        expected = SparseArray._simple_new(np.array([0, 2], dtype=np.int64),371                                           IntIndex(4, [2, 3]),372                                           dtype)373        tm.assert_sp_array_equal(result, expected)374        arr = SparseArray([0, np.nan, 0, 1], fill_value=0)375        with pytest.raises(ValueError, match='NA'):376            arr.astype('Sparse[i8]')377    def test_astype_bool(self):378        a = pd.SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0))379        result = a.astype(bool)380        expected = SparseArray([True, 0, 0, True],381                               dtype=SparseDtype(bool, 0))382        tm.assert_sp_array_equal(result, expected)383        # update fill value384        result = a.astype(SparseDtype(bool, False))385        expected = SparseArray([True, False, False, True],386                               dtype=SparseDtype(bool, False))387        tm.assert_sp_array_equal(result, expected)388    def test_astype_all(self, any_real_dtype):389        vals = np.array([1, 2, 3])390        arr = SparseArray(vals, fill_value=1)391        typ = np.dtype(any_real_dtype)392        res = arr.astype(typ)393        assert res.dtype == SparseDtype(typ, 1)394        assert res.sp_values.dtype == typ395        tm.assert_numpy_array_equal(np.asarray(res.values),396                                    vals.astype(typ))397    @pytest.mark.parametrize('array, dtype, expected', [398        (SparseArray([0, 1]), 'float',399         SparseArray([0., 1.], dtype=SparseDtype(float, 0.0))),400        (SparseArray([0, 1]), bool, SparseArray([False, True])),401        (SparseArray([0, 1], fill_value=1), bool,402         SparseArray([False, True], dtype=SparseDtype(bool, True))),403        pytest.param(404            SparseArray([0, 1]), 'datetime64[ns]',405            SparseArray(np.array([0, 1], dtype='datetime64[ns]'),406                        dtype=SparseDtype('datetime64[ns]',407                                          pd.Timestamp('1970'))),408            marks=[pytest.mark.xfail(reason="NumPy-7619")],409        ),410        (SparseArray([0, 1, 10]), str,411         SparseArray(['0', '1', '10'], dtype=SparseDtype(str, '0'))),412        (SparseArray(['10', '20']), float, SparseArray([10.0, 20.0])),413        (SparseArray([0, 1, 0]), object,414         SparseArray([0, 1, 0], dtype=SparseDtype(object, 0))),415    ])416    def test_astype_more(self, array, dtype, expected):417        result = array.astype(dtype)418        tm.assert_sp_array_equal(result, expected)419    def test_astype_nan_raises(self):420        arr = SparseArray([1.0, np.nan])421        with pytest.raises(ValueError, match='Cannot convert non-finite'):422            arr.astype(int)423    def test_set_fill_value(self):424        arr = SparseArray([1., np.nan, 2.], fill_value=np.nan)425        arr.fill_value = 2426        assert arr.fill_value == 2427        arr = SparseArray([1, 0, 2], fill_value=0, dtype=np.int64)428        arr.fill_value = 2429        assert arr.fill_value == 2430        # XXX: this seems fine? You can construct an integer431        # sparsearray with NaN fill value, why not update one?432        # coerces to int433        # msg = "unable to set fill_value 3\\.1 to int64 dtype"434        # with pytest.raises(ValueError, match=msg):435        arr.fill_value = 3.1436        assert arr.fill_value == 3.1437        # msg = "unable to set fill_value nan to int64 dtype"438        # with pytest.raises(ValueError, match=msg):439        arr.fill_value = np.nan440        assert np.isnan(arr.fill_value)441        arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool)442        arr.fill_value = True443        assert arr.fill_value444        # coerces to bool445        # msg = "unable to set fill_value 0 to bool dtype"446        # with pytest.raises(ValueError, match=msg):447        arr.fill_value = 0448        assert arr.fill_value == 0449        # msg = "unable to set fill_value nan to bool dtype"450        # with pytest.raises(ValueError, match=msg):451        arr.fill_value = np.nan452        assert np.isnan(arr.fill_value)453    @pytest.mark.parametrize("val", [[1, 2, 3], np.array([1, 2]), (1, 2, 3)])454    def test_set_fill_invalid_non_scalar(self, val):455        arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool)456        msg = "fill_value must be a scalar"457        with pytest.raises(ValueError, match=msg):458            arr.fill_value = val459    def test_copy_shallow(self):460        arr2 = self.arr.copy(deep=False)461        assert arr2.sp_values is self.arr.sp_values462        assert arr2.sp_index is self.arr.sp_index463    def test_values_asarray(self):464        assert_almost_equal(self.arr.values, self.arr_data)465        assert_almost_equal(self.arr.to_dense(), self.arr_data)466    @pytest.mark.parametrize('data,shape,dtype', [467        ([0, 0, 0, 0, 0], (5,), None),468        ([], (0,), None),469        ([0], (1,), None),470        (['A', 'A', np.nan, 'B'], (4,), np.object)471    ])472    def test_shape(self, data, shape, dtype):473        # GH 21126474        out = SparseArray(data, dtype=dtype)475        assert out.shape == shape476    @pytest.mark.parametrize("vals", [477        [np.nan, np.nan, np.nan, np.nan, np.nan],478        [1, np.nan, np.nan, 3, np.nan],479        [1, np.nan, 0, 3, 0],480    ])481    @pytest.mark.parametrize("method", ["to_dense", "get_values"])482    @pytest.mark.parametrize("fill_value", [None, 0])483    def test_dense_repr(self, vals, fill_value, method):484        vals = np.array(vals)485        arr = SparseArray(vals, fill_value=fill_value)486        dense_func = getattr(arr, method)487        res = dense_func()488        tm.assert_numpy_array_equal(res, vals)489    def test_getitem(self):490        def _checkit(i):491            assert_almost_equal(self.arr[i], self.arr.values[i])492        for i in range(len(self.arr)):493            _checkit(i)494            _checkit(-i)495    def test_getitem_arraylike_mask(self):496        arr = SparseArray([0, 1, 2])497        result = arr[[True, False, True]]498        expected = SparseArray([0, 2])499        tm.assert_sp_array_equal(result, expected)500    def test_getslice(self):501        result = self.arr[:-3]502        exp = SparseArray(self.arr.values[:-3])503        tm.assert_sp_array_equal(result, exp)504        result = self.arr[-4:]505        exp = SparseArray(self.arr.values[-4:])506        tm.assert_sp_array_equal(result, exp)507        # two corner cases from Series508        result = self.arr[-12:]509        exp = SparseArray(self.arr)510        tm.assert_sp_array_equal(result, exp)511        result = self.arr[:-12]512        exp = SparseArray(self.arr.values[:0])513        tm.assert_sp_array_equal(result, exp)514    def test_getslice_tuple(self):515        dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0])516        sparse = SparseArray(dense)517        res = sparse[4:, ]518        exp = SparseArray(dense[4:, ])519        tm.assert_sp_array_equal(res, exp)520        sparse = SparseArray(dense, fill_value=0)521        res = sparse[4:, ]522        exp = SparseArray(dense[4:, ], fill_value=0)523        tm.assert_sp_array_equal(res, exp)524        with pytest.raises(IndexError):525            sparse[4:, :]526        with pytest.raises(IndexError):527            # check numpy compat528            dense[4:, :]529    def test_boolean_slice_empty(self):530        arr = pd.SparseArray([0, 1, 2])531        res = arr[[False, False, False]]532        assert res.dtype == arr.dtype533    @pytest.mark.parametrize("op", ["add", "sub", "mul",534                                    "truediv", "floordiv", "pow"])535    def test_binary_operators(self, op):536        op = getattr(operator, op)537        data1 = np.random.randn(20)538        data2 = np.random.randn(20)539        data1[::2] = np.nan540        data2[::3] = np.nan541        arr1 = SparseArray(data1)542        arr2 = SparseArray(data2)543        data1[::2] = 3544        data2[::3] = 3545        farr1 = SparseArray(data1, fill_value=3)546        farr2 = SparseArray(data2, fill_value=3)547        def _check_op(op, first, second):548            res = op(first, second)549            exp = SparseArray(op(first.values, second.values),550                              fill_value=first.fill_value)551            assert isinstance(res, SparseArray)552            assert_almost_equal(res.values, exp.values)553            res2 = op(first, second.values)554            assert isinstance(res2, SparseArray)555            tm.assert_sp_array_equal(res, res2)556            res3 = op(first.values, second)557            assert isinstance(res3, SparseArray)558            tm.assert_sp_array_equal(res, res3)559            res4 = op(first, 4)560            assert isinstance(res4, SparseArray)561            # Ignore this if the actual op raises (e.g. pow).562            try:563                exp = op(first.values, 4)564                exp_fv = op(first.fill_value, 4)565            except ValueError:566                pass567            else:568                assert_almost_equal(res4.fill_value, exp_fv)569                assert_almost_equal(res4.values, exp)570        with np.errstate(all="ignore"):571            for first_arr, second_arr in [(arr1, arr2), (farr1, farr2)]:572                _check_op(op, first_arr, second_arr)573    def test_pickle(self):574        def _check_roundtrip(obj):575            unpickled = tm.round_trip_pickle(obj)576            tm.assert_sp_array_equal(unpickled, obj)577        _check_roundtrip(self.arr)578        _check_roundtrip(self.zarr)579    def test_generator_warnings(self):580        sp_arr = SparseArray([1, 2, 3])581        with warnings.catch_warnings(record=True) as w:582            warnings.filterwarnings(action='always',583                                    category=DeprecationWarning)584            warnings.filterwarnings(action='always',585                                    category=PendingDeprecationWarning)586            for _ in sp_arr:587                pass588            assert len(w) == 0589    def test_fillna(self):590        s = SparseArray([1, np.nan, np.nan, 3, np.nan])591        res = s.fillna(-1)592        exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64)593        tm.assert_sp_array_equal(res, exp)594        s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)595        res = s.fillna(-1)596        exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64)597        tm.assert_sp_array_equal(res, exp)598        s = SparseArray([1, np.nan, 0, 3, 0])599        res = s.fillna(-1)600        exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64)601        tm.assert_sp_array_equal(res, exp)602        s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0)603        res = s.fillna(-1)604        exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64)605        tm.assert_sp_array_equal(res, exp)606        s = SparseArray([np.nan, np.nan, np.nan, np.nan])607        res = s.fillna(-1)608        exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64)609        tm.assert_sp_array_equal(res, exp)610        s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0)611        res = s.fillna(-1)612        exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64)613        tm.assert_sp_array_equal(res, exp)614        # float dtype's fill_value is np.nan, replaced by -1615        s = SparseArray([0., 0., 0., 0.])616        res = s.fillna(-1)617        exp = SparseArray([0., 0., 0., 0.], fill_value=-1)618        tm.assert_sp_array_equal(res, exp)619        # int dtype shouldn't have missing. No changes.620        s = SparseArray([0, 0, 0, 0])621        assert s.dtype == SparseDtype(np.int64)622        assert s.fill_value == 0623        res = s.fillna(-1)624        tm.assert_sp_array_equal(res, s)625        s = SparseArray([0, 0, 0, 0], fill_value=0)626        assert s.dtype == SparseDtype(np.int64)627        assert s.fill_value == 0628        res = s.fillna(-1)629        exp = SparseArray([0, 0, 0, 0], fill_value=0)630        tm.assert_sp_array_equal(res, exp)631        # fill_value can be nan if there is no missing hole.632        # only fill_value will be changed633        s = SparseArray([0, 0, 0, 0], fill_value=np.nan)634        assert s.dtype == SparseDtype(np.int64, fill_value=np.nan)635        assert np.isnan(s.fill_value)636        res = s.fillna(-1)637        exp = SparseArray([0, 0, 0, 0], fill_value=-1)638        tm.assert_sp_array_equal(res, exp)639    def test_fillna_overlap(self):640        s = SparseArray([1, np.nan, np.nan, 3, np.nan])641        # filling with existing value doesn't replace existing value with642        # fill_value, i.e. existing 3 remains in sp_values643        res = s.fillna(3)644        exp = np.array([1, 3, 3, 3, 3], dtype=np.float64)645        tm.assert_numpy_array_equal(res.to_dense(), exp)646        s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)647        res = s.fillna(3)648        exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64)649        tm.assert_sp_array_equal(res, exp)650    def test_nonzero(self):651        # Tests regression #21172.652        sa = pd.SparseArray([653            float('nan'),654            float('nan'),655            1, 0, 0,656            2, 0, 0, 0,657            3, 0, 0658        ])659        expected = np.array([2, 5, 9], dtype=np.int32)660        result, = sa.nonzero()661        tm.assert_numpy_array_equal(expected, result)662        sa = pd.SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])663        result, = sa.nonzero()664        tm.assert_numpy_array_equal(expected, result)665class TestSparseArrayAnalytics(object):666    @pytest.mark.parametrize('data,pos,neg', [667        ([True, True, True], True, False),668        ([1, 2, 1], 1, 0),669        ([1.0, 2.0, 1.0], 1.0, 0.0)670    ])671    def test_all(self, data, pos, neg):672        # GH 17570673        out = SparseArray(data).all()674        assert out675        out = SparseArray(data, fill_value=pos).all()676        assert out677        data[1] = neg678        out = SparseArray(data).all()679        assert not out680        out = SparseArray(data, fill_value=pos).all()681        assert not out682    @pytest.mark.parametrize('data,pos,neg', [683        ([True, True, True], True, False),684        ([1, 2, 1], 1, 0),685        ([1.0, 2.0, 1.0], 1.0, 0.0)686    ])687    @td.skip_if_np_lt_115  # prior didn't dispatch688    def test_numpy_all(self, data, pos, neg):689        # GH 17570690        out = np.all(SparseArray(data))691        assert out692        out = np.all(SparseArray(data, fill_value=pos))693        assert out694        data[1] = neg695        out = np.all(SparseArray(data))696        assert not out697        out = np.all(SparseArray(data, fill_value=pos))698        assert not out699        # raises with a different message on py2.700        msg = "the \'out\' parameter is not supported"701        with pytest.raises(ValueError, match=msg):702            np.all(SparseArray(data), out=np.array([]))703    @pytest.mark.parametrize('data,pos,neg', [704        ([False, True, False], True, False),705        ([0, 2, 0], 2, 0),706        ([0.0, 2.0, 0.0], 2.0, 0.0)707    ])708    def test_any(self, data, pos, neg):709        # GH 17570710        out = SparseArray(data).any()711        assert out712        out = SparseArray(data, fill_value=pos).any()713        assert out714        data[1] = neg715        out = SparseArray(data).any()716        assert not out717        out = SparseArray(data, fill_value=pos).any()718        assert not out719    @pytest.mark.parametrize('data,pos,neg', [720        ([False, True, False], True, False),721        ([0, 2, 0], 2, 0),722        ([0.0, 2.0, 0.0], 2.0, 0.0)723    ])724    @td.skip_if_np_lt_115  # prior didn't dispatch725    def test_numpy_any(self, data, pos, neg):726        # GH 17570727        out = np.any(SparseArray(data))728        assert out729        out = np.any(SparseArray(data, fill_value=pos))730        assert out731        data[1] = neg732        out = np.any(SparseArray(data))733        assert not out734        out = np.any(SparseArray(data, fill_value=pos))735        assert not out736        msg = "the \'out\' parameter is not supported"737        with pytest.raises(ValueError, match=msg):738            np.any(SparseArray(data), out=out)739    def test_sum(self):740        data = np.arange(10).astype(float)741        out = SparseArray(data).sum()742        assert out == 45.0743        data[5] = np.nan744        out = SparseArray(data, fill_value=2).sum()745        assert out == 40.0746        out = SparseArray(data, fill_value=np.nan).sum()747        assert out == 40.0748    def test_numpy_sum(self):749        data = np.arange(10).astype(float)750        out = np.sum(SparseArray(data))751        assert out == 45.0752        data[5] = np.nan753        out = np.sum(SparseArray(data, fill_value=2))754        assert out == 40.0755        out = np.sum(SparseArray(data, fill_value=np.nan))756        assert out == 40.0757        msg = "the 'dtype' parameter is not supported"758        with pytest.raises(ValueError, match=msg):759            np.sum(SparseArray(data), dtype=np.int64)760        msg = "the 'out' parameter is not supported"761        with pytest.raises(ValueError, match=msg):762            np.sum(SparseArray(data), out=out)763    @pytest.mark.parametrize("data,expected", [764        (np.array([1, 2, 3, 4, 5], dtype=float),  # non-null data765         SparseArray(np.array([1.0, 3.0, 6.0, 10.0, 15.0]))),766        (np.array([1, 2, np.nan, 4, 5], dtype=float),  # null data767         SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0])))768    ])769    @pytest.mark.parametrize("numpy", [True, False])770    def test_cumsum(self, data, expected, numpy):771        cumsum = np.cumsum if numpy else lambda s: s.cumsum()772        out = cumsum(SparseArray(data))773        tm.assert_sp_array_equal(out, expected)774        out = cumsum(SparseArray(data, fill_value=np.nan))775        tm.assert_sp_array_equal(out, expected)776        out = cumsum(SparseArray(data, fill_value=2))777        tm.assert_sp_array_equal(out, expected)778        if numpy:  # numpy compatibility checks.779            msg = "the 'dtype' parameter is not supported"780            with pytest.raises(ValueError, match=msg):781                np.cumsum(SparseArray(data), dtype=np.int64)782            msg = "the 'out' parameter is not supported"783            with pytest.raises(ValueError, match=msg):784                np.cumsum(SparseArray(data), out=out)785        else:786            axis = 1  # SparseArray currently 1-D, so only axis = 0 is valid.787            msg = "axis\\(={axis}\\) out of bounds".format(axis=axis)788            with pytest.raises(ValueError, match=msg):789                SparseArray(data).cumsum(axis=axis)790    def test_mean(self):791        data = np.arange(10).astype(float)792        out = SparseArray(data).mean()793        assert out == 4.5794        data[5] = np.nan795        out = SparseArray(data).mean()796        assert out == 40.0 / 9797    def test_numpy_mean(self):798        data = np.arange(10).astype(float)799        out = np.mean(SparseArray(data))800        assert out == 4.5801        data[5] = np.nan802        out = np.mean(SparseArray(data))803        assert out == 40.0 / 9804        msg = "the 'dtype' parameter is not supported"805        with pytest.raises(ValueError, match=msg):806            np.mean(SparseArray(data), dtype=np.int64)807        msg = "the 'out' parameter is not supported"808        with pytest.raises(ValueError, match=msg):809            np.mean(SparseArray(data), out=out)810    def test_ufunc(self):811        # GH 13853 make sure ufunc is applied to fill_value812        sparse = SparseArray([1, np.nan, 2, np.nan, -2])813        result = SparseArray([1, np.nan, 2, np.nan, 2])814        tm.assert_sp_array_equal(abs(sparse), result)815        tm.assert_sp_array_equal(np.abs(sparse), result)816        sparse = SparseArray([1, -1, 2, -2], fill_value=1)817        result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index,818                             fill_value=1)819        tm.assert_sp_array_equal(abs(sparse), result)820        tm.assert_sp_array_equal(np.abs(sparse), result)821        sparse = SparseArray([1, -1, 2, -2], fill_value=-1)822        result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index,823                             fill_value=1)824        tm.assert_sp_array_equal(abs(sparse), result)825        tm.assert_sp_array_equal(np.abs(sparse), result)826        sparse = SparseArray([1, np.nan, 2, np.nan, -2])827        result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2]))828        tm.assert_sp_array_equal(np.sin(sparse), result)829        sparse = SparseArray([1, -1, 2, -2], fill_value=1)830        result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1))831        tm.assert_sp_array_equal(np.sin(sparse), result)832        sparse = SparseArray([1, -1, 0, -2], fill_value=0)833        result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0))834        tm.assert_sp_array_equal(np.sin(sparse), result)835    def test_ufunc_args(self):836        # GH 13853 make sure ufunc is applied to fill_value, including its arg837        sparse = SparseArray([1, np.nan, 2, np.nan, -2])838        result = SparseArray([2, np.nan, 3, np.nan, -1])839        tm.assert_sp_array_equal(np.add(sparse, 1), result)840        sparse = SparseArray([1, -1, 2, -2], fill_value=1)841        result = SparseArray([2, 0, 3, -1], fill_value=2)842        tm.assert_sp_array_equal(np.add(sparse, 1), result)843        sparse = SparseArray([1, -1, 0, -2], fill_value=0)844        result = SparseArray([2, 0, 1, -1], fill_value=1)845        tm.assert_sp_array_equal(np.add(sparse, 1), result)846    def test_nbytes_integer(self):847        arr = SparseArray([1, 0, 0, 0, 2], kind='integer')848        result = arr.nbytes849        # (2 * 8) + 2 * 4850        assert result == 24851    def test_nbytes_block(self):852        arr = SparseArray([1, 2, 0, 0, 0], kind='block')853        result = arr.nbytes854        # (2 * 8) + 4 + 4855        # sp_values, blocs, blenghts856        assert result == 24857    def test_asarray_datetime64(self):858        s = pd.SparseArray(859            pd.to_datetime(['2012', None, None, '2013'])860        )861        np.asarray(s)862    def test_density(self):863        arr = SparseArray([0, 1])864        assert arr.density == 0.5865    def test_npoints(self):866        arr = SparseArray([0, 1])867        assert arr.npoints == 1868class TestAccessor(object):869    @pytest.mark.parametrize('attr', [870        'npoints', 'density', 'fill_value', 'sp_values',871    ])872    def test_get_attributes(self, attr):873        arr = SparseArray([0, 1])874        ser = pd.Series(arr)875        result = getattr(ser.sparse, attr)876        expected = getattr(arr, attr)877        assert result == expected878    def test_from_coo(self):879        sparse = pytest.importorskip("scipy.sparse")880        row = [0, 3, 1, 0]881        col = [0, 3, 1, 2]882        data = [4, 5, 7, 9]883        sp_array = sparse.coo_matrix((data, (row, col)))884        result = pd.Series.sparse.from_coo(sp_array)885        index = pd.MultiIndex.from_arrays([[0, 0, 1, 3], [0, 2, 1, 3]])886        expected = pd.Series([4, 9, 7, 5], index=index, dtype='Sparse[int]')887        tm.assert_series_equal(result, expected)888    def test_to_coo(self):889        sparse = pytest.importorskip("scipy.sparse")890        ser = pd.Series([1, 2, 3],891                        index=pd.MultiIndex.from_product([[0], [1, 2, 3]],892                                                         names=['a', 'b']),893                        dtype='Sparse[int]')894        A, _, _ = ser.sparse.to_coo()895        assert isinstance(A, sparse.coo.coo_matrix)896    def test_non_sparse_raises(self):897        ser = pd.Series([1, 2, 3])898        with pytest.raises(AttributeError, match='.sparse'):899            ser.sparse.density900def test_setting_fill_value_fillna_still_works():901    # This is why letting users update fill_value / dtype is bad902    # astype has the same problem.903    arr = SparseArray([1., np.nan, 1.0], fill_value=0.0)904    arr.fill_value = np.nan905    result = arr.isna()906    # Can't do direct comparison, since the sp_index will be different907    # So let's convert to ndarray and check there.908    result = np.asarray(result)909    expected = np.array([False, True, False])910    tm.assert_numpy_array_equal(result, expected)911def test_setting_fill_value_updates():912    arr = SparseArray([0.0, np.nan], fill_value=0)913    arr.fill_value = np.nan914    # use private constructor to get the index right915    # otherwise both nans would be un-stored.916    expected = SparseArray._simple_new(917        sparse_array=np.array([np.nan]),918        sparse_index=IntIndex(2, [1]),919        dtype=SparseDtype(float, np.nan),920    )921    tm.assert_sp_array_equal(arr, expected)922@pytest.mark.parametrize("arr, loc", [923    ([None, 1, 2], 0),924    ([0, None, 2], 1),925    ([0, 1, None], 2),926    ([0, 1, 1, None, None], 3),927    ([1, 1, 1, 2], -1),928    ([], -1),929])930def test_first_fill_value_loc(arr, loc):931    result = SparseArray(arr)._first_fill_value_loc()932    assert result == loc933@pytest.mark.parametrize('arr', [934    [1, 2, np.nan, np.nan],935    [1, np.nan, 2, np.nan],936    [1, 2, np.nan],937])938@pytest.mark.parametrize("fill_value", [939    np.nan, 0, 1940])941def test_unique_na_fill(arr, fill_value):942    a = pd.SparseArray(arr, fill_value=fill_value).unique()943    b = pd.Series(arr).unique()944    assert isinstance(a, SparseArray)945    a = np.asarray(a)946    tm.assert_numpy_array_equal(a, b)947def test_unique_all_sparse():948    # https://github.com/pandas-dev/pandas/issues/23168949    arr = SparseArray([0, 0])950    result = arr.unique()951    expected = SparseArray([0])952    tm.assert_sp_array_equal(result, expected)953def test_map():954    arr = SparseArray([0, 1, 2])955    expected = SparseArray([10, 11, 12], fill_value=10)956    # dict957    result = arr.map({0: 10, 1: 11, 2: 12})958    tm.assert_sp_array_equal(result, expected)959    # series960    result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))961    tm.assert_sp_array_equal(result, expected)962    # function963    result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))964    expected = SparseArray([10, 11, 12], fill_value=10)965    tm.assert_sp_array_equal(result, expected)966def test_map_missing():967    arr = SparseArray([0, 1, 2])968    expected = SparseArray([10, 11, None], fill_value=10)969    result = arr.map({0: 10, 1: 11})...

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